Wildfire effects on ecosystem services in two disparate California watersheds: A Case Study

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Sloggy, Mani Rouhi Rad, Debabrata Sahoo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4189499/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 May, 2025 Read the published version in Environmental Management → Version 1 posted 8 You are reading this latest preprint version Abstract Ecosystem services are important for human well-being and maintaining environmental quality objectives. The growing concern over extreme wildfire events in various watersheds necessitates understanding their impacts particularly on regulating ecosystems services. In this study, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to examine how two wildfires that occurred in California, USA in 2017 impacted water provisioning, soil loss and sediment delivery, carbon sequestration services, and nutrient delivery in the waterways. We also related the distributional impacts of wildfire to ecosystem service supply based on various sociodemographic factors across the affected communities to assess their vulnerabilities. We find that a year following the fires, the amount of biomass in forestland, woodland, and chaparral declined, as expected, in both studied watersheds, while the amount of grassland increased. This change in vegetation resulted in the loss of about 200,000 tons of carbon from the Mark West subwatershed and about 160,000 tons of carbon from the Southern California watersheds. Furthermore, the fires increased the expected mean annual water yield significantly for both watersheds by 5% and 42%, respectively. Our analysis shows an increase in the expected post-fire phosphorus and nitrogen export. Using regression analyses to determine the effect of wildfire on the distributional impacts to ecosystem services across communities in the watersheds, we did find evidence of differences between communities with respect to the pre-fire distribution of ecosystem services. However, we did not find that post-fire condition either exacerbated or alleviated these distributional impacts and inequities. Fire impacts Ecosystem Services Soil Carbon InVEST Tubbs Fire Thomas Fire Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1.0. Introduction Watershed ecosystems provide a variety of ecosystem services including carbon sequestration, water quantity and quality improvement, and regulation of pollution levels (Brockerhoff et al., 2017 ). These ecosystem services are essential for maintaining life and ensuring management and environmental quality goals. A major threat to of the sustainable supply of ecosystem services is the increased frequency and severity of wildfires due to land use and climate change, in more arid regions of the Western US (Westerling et al., 2006 ), Australia (Haque et al., 2021 ), Western and Southern Europe (Dupuy et al., 2021) and South America (Ciocca et al., 2023 ). While wildfires are part of many properly functioning ecosystems (Lecina-Diaz et al., 2021 ), changing wildfire regimes will likely result in changing levels of ecosystem service provision on severely and frequently fire-affected watersheds (Pereira et al., 2021 ). This paper studies the effects of wildfires on the regulating ecosystem services from Forest along with the change in the distributional impacts of ecosystem services across local communities. The study area includes two fire-affected landscapes: the Mark-West subwatershed before and after the Tubbs fire, and Harmon Canyon, Arundell, and Ventura subwatersheds in Ventura and Santa Barbara County which were impacted by the Thomas Fire. The diversity in watersheds provided by these two areas makes them useful for this topic. In addition, California has experienced an increase in the frequency and severity of wildfires throughout the state partly to climate change as well as human-driven factors (Westerling and Bryant, 2008 ). Under climate change, the state may observe further increases in wildfire risk in the future (Westerling and Bryant, 2008 ). This makes it an ideal and important area to consider when studying how fires might impact the equitable and sustainable distributions of ecosystem services. Post wildfire events lead to increased soil loss and sediment export including water quality degradation, reduction of nutrient-enriched soil, habitat destruction, reduced soil productivity, and water scarcity (CITE). Soil loss and transportation into rivers, lakes, and reservoirs compromise water quality by increasing turbidity and introducing pollutants, which could contaminate the water (Issaka and Ashraf 2017 ). Soil erosion can also lead to the washing away of essential topsoil, which is enriched with nutrients, reducing soil fertility, and impacting plant growth and productivity (Orgiazzi and Panagos, 2018 ). This can result in a significant loss of agricultural yield and affect food and resource availability. Sedimentation of receiving rivers, reservoirs, ponds, and channels is often attributed to increased soil loss from the watershed, and sediment export reduces the water carrying capacity of the waterbody and causes overflowing off their banks, leading to flooding (Uri, 2001 ). Soil erosion also threatens terrestrial biodiversity by impacting communities of the fauna inhabiting the soil through habitat degradation (Guerra et al. 2020 ), which could affect ecosystem functioning. We contribute to the literature on the effects of wildfires in two ways. First, several studies have analyzed the effects of fires on ecosystem services provided by forested watersheds. Lecina-Diaz et al. ( 2021 ) studied the risk to forest-based ecosystem services based on a hazard index that depends on the availability of exposed ecosystems from the forests, the probability of fire hazard from weather, and the capacity of the forest to recover after a fire. Lee et al. ( 2015 ) studied the benefits of climate change mitigation by studying their benefits on reducing the loss of ecosystem services from forests. They used a habitat equivalency analysis that estimates the loss of ecosystem services as acre-years of lost vegetation and considered the avoided cost of fuel treatment as benefit of mitigation. We contribute to this literature by studying the changes in regulating ecosystem services provided by forests across watersheds in two different regions of California. Second, though several studies have examined how ecosystem services are distributed across human settlements (Plieninger 2013; Fu et al., 2015 ), and many have addressed the effects of wildfire on ecosystem services (e.g., Vukomanovic and Steelman, 2019 ), there remains a gap in examining how wildfires change how ecosystem services are spatially and equitable distributed across different communities in a watershed (Yadav et al., 2023 ). Core to this paper and many others examining environmental justice are the concepts of distributional impacts and community well-being Thomas et al., 2022 ). In this study, we examine specifically the distribution of ecosystem services, which are defined as benefits received by individuals that flow from environmental sources (Chen et al., 2023). The ecosystem services are distributed spatially across the landscape, which influences how they are distributed across different sociodemographic groups. In addition to ecosystem services, the landscape can also produce ecosystem disservices, which are defined as costs incurred by individuals as opposed to benefits (Escobedo et al., 2011). Wildfire changes the spatial distribution of the ecosystem services, and thus changes the distributional impacts of these as well. This aim of this paper was to model the spatiotemporal and distributional impacts of wildfires on watershed-scale ecosystem services across different communities. The specific objective of this study is to 1) assess the effects of two wildfires in different ecoregions and watersheds in California had on: i. water quantity, ii. soil loss and sediment delivery, iii. carbon sequestration, iv. nutrient delivery and 2) understand the pre- and post-fire spatial and distributional impacts to ES supply across different sociodemographic groups. The main contribution of this study is the novel linkage of fire and ecosystem service modeling to sociodemographic analysis to estimate the distributional impacts of wildfires on ecosystem services. 2.0. Methodology 2.1. Study Area Our study focuses on the Mark West subwatershed in northern California, and the Harmon, Arundel and Ventura subwatersheds in southern California hereafter called Southern California watershed (Fig. 1 ). These watersheds were chosen because they were both subject to large wildfires that greatly impacted the area and the two watersheds are in two different ecoregions. In addition, the differences in size, climate, land cover, and other physical attributes between these two watersheds can be used to better understand the heterogeneity in the effects of wildfires on ecosystem services. For example, the Mark West subwatershed is considerably smaller (14,767 hectares) than the Southern California watershed (26,379 hectares). The Mark West subwatershed is at an altitude of between 5 meters and 850 meters above sea level (Woolfenden et al., 2011 ). The watershed of the Mark West Creek has a mediterranean climate and is part of a larger region that is characterized by several features including oak woodlands, grasslands, and riparian woodlands (Potter and Hiatt, 2009 ) The Southern California watershed is coastal and has a maximum altitude of 1833 meters at its headwaters and has a Mediterranean climate and characterized by large amounts of chaparral vegetation (Jumps et al., 2022 ). The Mark West subwatershed was burned during the 2017 Tubbs fire. The Tubbs fire burned through parts of Sonoma County, entering many populated areas including Santa Rosa, Sonoma county’s largest city (Cortenbach et al., 2019 ). The fire destroyed over 5,643 structures and 22 people lost their lives (LeComte, 2018 ). The Southern California watershed also burned in 2017 during the Thomas fire. The fire burned through parts of Ventura and Santa Barbara counties (Kolden and Henson, 2019 ) and resulted in a large landslide that destroyed parts of the highway 101 south of Santa Barbara (Lukashov et al., 2019 ). The fire and mudslide that followed took the lives of 23 people (Kress, 2020 ). More detailed accounts and descriptions of both fires are found in supplementary file. 2.2. Fire Effects Modeling Process The fire effects modeling process was designed to provide pre- and post-burn above ground biomass data for the 2017 Tubbs fire in northern California and the 2017 Thomas Fire in southern California. Both fires started outside the wildland fire interface (WUI) and burned through the WUI into urban areas in Santa Rosa Ca and Ventura CA respectively. In this process, fire perimeter data is downloaded from the monitoring trends in burn severity project (MTBS; Eidenshink 2007). Pre-burn above ground biomass is estimated using the fuel characteristic classification system maps (Fuel Characteristic Classification System; Ottmar et al. 2007, Prichard et al. 2013) as provided by the LANDFIRE project data distribution site (Rollins 2009). Initial fire severity observations are downloaded from the Rapid Assessment of Vegetation Condition (RAVG; less than 1-month post-fire; Miller and Thode 2007, Miller and Quayle 2015) followed by downloading 1-year post-fire observations from MTBS (Eidenshink 2007). Post-burn above ground biomass is estimated based on biomass reduction equations using the fire severity observations (Prichard et al 2017) and the fire and fuels tools software package (Prichard et al 2013). Specifically, we used the fire perimeters created by MTBS to define the area affected by fire. The MTBS project uses Landsat earth observations taken of the general fire area before the fire and then after the fire in combination with fire perimeters gathered by GIS specialists to determine the fire perimeter which becomes the final perimeter of record. The study area was further defined to the areas covered by the Mark West Creek watershed for the Tubbs Fire and the Lower Ventura River, Arundell Barranca-Frontal Pacific, and the Harmon Canyon-Santa Clara River watersheds for the Thomas Fire from the California HUC12 watershed delineation maps. Each of these watersheds contained significant portions of urban, wildland urban interface (WUI) landscapes in the watershed that were burned by wildfire. Pre-fire vegetation type was estimated for each 30 square meter pixel within the watershed boundaries using the Fuel Characteristic Classification System (FCCS) layer included in the 2016 release of the LANDFIRE fuels and vegetation layers ( www.landfire.gov ) and the LANDFIRE existing vegetation layer (EVT: Rollins 2009). The LANDFIRE EVT and the LANDFIRE FCCS layers each contain vegetation type descriptions that progress from generic coarse scale vegetation type descriptions such as “shrubland”, “conifer” or “hardwood” to fine scaled descriptive names including “California Coastal Live Oak Woodland and Savanna”. To simplify the analysis, we used the more generic coarse scale descriptions since few pixels in our landscapes contained the more specific vegetation types. Above ground biomass was estimated using the LANDFIRE FCCS layer and associated database (Pritchard et al. 2017). FCCS provides biomass estimates which can be summed into the following broad categories: tree, shrub, herbaceous, downed and dead logs, and forest floor biomass (new and decomposed biomass). FCCS provides biomass estimates in biomass per unit area such as tons per acre (Ottmar et al. 2007). We used ArcMap 10.5 and excel spreadsheets to link the EVT maps with the FCCS maps to produce the pre-burn estimates by vegetation type for each of our two large watersheds. Biomass changes across the fire affected landscapes in the watersheds were estimated using fire severity metrics provided by MTBS (Eidenshink et al. 2007 ). Fire severity is estimated by quantifying vegetation reflectance differences where Landsat imagery is compared before and after fire using the relative differenced Normalized Burn Ratio (RdNBR; Eidenshink et al. 2007 ). The quantified difference is then related back to the original vegetation to estimate changes in vegetation condition, status (live or dead), and biomass consumed (Drury et al. 2014, Prichard et al. 2017). This methodology does not enable us to determine vegetation type changes but does provide tools to estimate biomass remaining on the landscape after burning (Drury et al. 2014, Prichard et al. 2017). Specifically, we combined the fire severity maps with post-fire biomass remaining calculations produced by the FCCS Fire and Fuels Tools (Ottmar et al. 2009) and post-fire biomass equations developed for the LANDFIRE mapping project (Prichard et al. 2017) using ArcGIS 10.5 to produce a custom set of biomasses remaining in the fire affected areas of the Tubbs and Thomas fire perimeters. The resulting pre- and post-burn biomass maps serve as inputs into the INVEST model described below. 2.3. Ecosystem Services Modelling We used four different InVEST modules to model five different ecosystem services that are affected by wildfires: 1) carbon storage, 2) carbon sequestration, 3) annual water yield, 4) sediment delivery, and 5) nutrient delivery (phosphorus and nitrogen). The InVEST model have previously simulated the effect of climate change and land use land cover change on ecosystem services like water yield and supply (Clerici et al., 2019 ; Fu et al., 2017 ). Changes in each ecosystem service in this study were estimated by comparing modeled ecosystem services before and after fire for each watershed. Land cover alterations that occur after the fire event describe the impact of the fire in each watershed on existing vegetation cover. We first estimated the outcome of each ecosystem process using the vegetation cover prior to the fire for each watershed. We then estimated the ecosystem outcomes for the vegetation cover one year after the fire. The difference between the two provided us with the change in ecosystem services as a result of the fires. The reason that we selected one year after each fire as our post-fire ecosystem service valuation is that vegetation changes are more stable a year after a fire takes place than considering day-to-day changes in vegetation and ecosystem services immediately after a fire, which may not provide an accurate representation of ecosystem service changes. We calculated the differences between the InVEST model outputs prior to and following the fire by loading both outputs as rasters into R (R Core Team, 2022 ) and using the raster package (Hijmans, 2023 ) to take the difference in values between the two rasters. Since the InVEST modules requires a set of different inputs so we parameterized each module using a variety of inputs listed in Table 1 . 2.3.1. Annual Water Yield The annual water yield module of InVEST quantifies the contribution of different parts of the watershed to the overall water reaching the outlet in a year (Wu et al., 2022 ). This refers to all forms of water movement that originate from precipitation, snowmelt, and other sources in the watershed. The annual water yield of a watershed is an essential ecosystem service that supports human life and development. The InVEST annual water yield module estimates the water yield for a watershed at the pixel-level and at the watershed-level water (Sharp et al., 2020 ). This module can also estimate the economic value of energy produced using the water supplied to the hydropower reservoir based on the contributions of water runoff from each landscape type (Sharp et al., 2020 ). Our analysis excludes the hydropower economic valuation component of the model because hydropower is not a major electricity producer in this region. Generally, the model determines the quantity of water yield per pixel as the difference between precipitation and evapotranspiration (ET). In this study, we estimate the change in the annual water yield of each watershed due to burning by using the InVEST annual water yield module to first estimate water runoff from each pixel and aggregate runoff for each watershed before and after the relevant fire event. Then, the difference between annual yield before and after a fire is presented as the effect of wildfires on the change in annual water yield. Table 1 Input data details Input Type Sources Units Rainfall Erosivity index (R) Raster Global Rainfall Erosivity Database MJ.mm.(ha.h.yr)-1 Soil Erodibility (K) Raster gSSURGO t.ha.hr.(MJ.mm.ha)-1 Digital Elevation Model (m) Raster SRTM m Precipitation Raster Daymet mm Land management factor Decimal Literature Unitless Cover factor Decimal Literature Unitless Plant Available Water Raster gSSURGO mm Reference Evapotranspiration Raster Daymet mm Depth To Root Restricting Layer Raster gSSURGO mm Borselli k Parameter Decimal Vigiak et al. 2012 Unitless Borselli IC0 Parameter Decimal Vigiak et al. 2012 Unitless Threshold flow accumulation Integer Vigiak et al. 2012 Unitless Max SDR Value Max SDR Value Vigiak et al. 2012 Unitless Successful estimation of the annual water yield module requires input data on precipitation, biophysical information, evapotranspiration, plant available water content, root restricting layer depth, and consumptive water use. Precipitation data was obtained from DayMet (Thornton et al., 2022 ) for the period of 1987 to 2017 based on the tile numbers that represent the study area. Daymet is a data-driven product that uses various algorithms for interpolation and extrapolation of daily meteorological parameters to produce gridded daily parameters at a spatial resolution of 1 km. Daily precipitation values in millimeters were summed by year and then cropped to the study watershed areas to obtain the average annual precipitation estimates. The minimum and maximum temperature and solar radiation were also downloaded from the same database and used for the estimation of the reference evapotranspiration and cover l(ETo) based on the modified Hargreaves equation. These meteorological variables used as model input are historical average across the 30 years of data acquisition. Data on Depth to Root Restricting Layer was obtained from the Gridded Soil Survey Geographic (gSSURGO) database. Similarly, Plant Available Water Content Fraction which is the ratio of actual ET and precipitation was obtained from the gSSURGO database. Watershed shapefiles were secured from the U.S. Geological Survey’s Watershed Boundary dataset while the same land use /land cover details (as in the case of the carbon model) were applied to the annual water yield model estimation. Additional data efforts focused on creating biophysical parameters and water demand information relevant to the water yield model estimation. Accumulated biophysical data includes land use/land cover codes for each landscape class, crop coefficients (Kc values), root depth, and Z-parameter. Land use/land cover codes are integers and remain the same as those used in the carbon model. Kc values for each land cover classification were secured from NISTOR et al. ( 2018 ). Root depth for each land cover type was also obtained from published studies (Canadell et al., 1996 ). Maximum root depth for each vegetation type measures the depth to which at least 95 percent of root biomass occurs. Finally, we calculated the Z-parameter using omega estimates based on the work of Xu et al. ( 2013 ), and the mean of precipitation and available water content earlier described. We focused on the Mark West subwatershed and the Arundel, Ventura and Harmon sub-watersheds representing the Southern California watersheds, estimating water yield, consumption, and scarcity before and after fire events. 2.3.2. Sediment Delivery By altering vegetation cover and litter, fires can also change the amount of sediment exported from a catchment (Warrick et al., 2012 ). The sediment delivery ratio module of the InVEST model was used to assess the annual sediment exported from the catchment to the outlet. The model uses a combination of the soil loss calculated through the revised universal soil loss equation (RUSLE) and the sediment delivery ratio (SDR), which quantifies the proportion of soil loss reaching the outlet. The model works explicitly on the spatial resolution of the digital elevation model (DEM) and performs its operation for each pixel (Sharp et al., 2020 ). The SDR estimation begins by computing the hydrological linkage between sediment sources and streams, often called connectivity index (IC), which is a function of the area upslope of each pixel and the flow path between the pixel and the nearest stream. The data sources for the InVEST SDR model ranges from literature, organizations, public reports and agencies. The rainfall erosivity index, (R hereafter), quantifies the intensity of rainfall to initiate soil loss and was obtained from the global erosivity map published by the Joint Research Centre of the European Commission. The map was a result of an extensive project focused on estimating rainfall erosivity across 63 countries (Panagos et al., 2017 ). The soil erodibility factor (K) was derived from the United States Department of Agriculture’s NRCS gSSURGO database and measures the ability of the soil to be eroded under standard condition. The R and K factor for the study sites were clipped out of the raster map obtained from their respective databases. The Digital Elevation Model (DEM) raster file was obtained from the Shuttle Radar Topography Mission (SRTM) database and processed using ArcGIS Pro 3.0.3. The Fill Sink tool in the software was used to fill the depressions in the DEM. The threshold flow accumulation and Borselli K parameter were set at 1000 and 2 respectively. The cover management factor (C), utilized in this study quantifies the ability of a land use type to resist erosion. This value ranges from 0 to 1, with values closer to 0 indicating that less erosion is likely to occur while values closer to 1 indicate more is likely to occur in the land use pixel. The C-factor for the pre-fire land cover map for the watersheds was obtained from ensemble sources including literature, sediment database provided in the InVEST User guide, and technical reports such as Tetra Tech ( 2015b ) and McKague ( 2023 ). The C-factor for post-fire land cover map was however derived based on results from published literature (Terranova et al., 2009 ), which assigns a C-factor to land cover based on burn severity. This study assigns C = 0.20 for severely burned areas, C = 0.05 for moderately burned areas, and C = 0.01 for areas that had burned at low severity. The support practice factor (P) was set for 1 since no land management practice was identified in the study site. The maximum theoretical SDR was set as 0.8 as recommended by Sharp et al. ( 2020 ), and the K and IC 0 parameters are set to 2 and 0.5 respectively as explained by Vigiak et al. ( 2012 ) 2.3.3. Carbon Storage and Sequestration Carbon sequestration and storage is an important ecosystem service provided by forests (Sohngen and Brown, 2006 ), and carbon emissions are a notable ecosystem disservice arising from wildfires (Simmonds et al., 2021 ). To evaluate the impacts of the Tubbs and Thomas fires on the carbon storage within our watersheds of interest, we first estimated changes in the biomass levels arising from wildfire-induced vegetation changes (see Section 3.1 ). We then compared the aboveground carbon levels across both scenarios. We assume no land use changes before and after each fire. In practice, there could be changes in land, which has been demonstrated in other studies (Mockrin et al., 2020 ). We convert above ground biomass (AGB) per acre to Carbon (or Carbon equivalent, as opposed to Carbon dioxide equivalent) per hectare using Eq. 1: $${C}_{hectare}=2.471* {AGB}_{acre}=1.2355*{AGB}_{acre} \left(1\right)$$ The factor 2.471 is the conversion from Acres to Hectares, and 0.5 is the factor that converts dry weight biomass to carbon (as opposed to Carbon Dioxide equivalent; Li et al. 2011 ; Wirasatriya et al. 2022 ). Once the conversion factor is applied to the biomass stores before and after the fire, we subtracted the total above ground carbon stored before the fire from the total above ground carbon stored after the fire and calculated the change in carbon pre- and post-fire. 2.3.4. Nutrient Delivery The nutrient delivery module of InVEST was used to quantify the export and retention of nitrogen and phosphorus across the watersheds and to identify changes in nutrient export under land cover conditions before and after the fires in the two study areas. The module uses the simple mass balance concept to describe the long steady state flow of nutrients using empirical relationships (Sharp et al., 2020 ). The model computes the nutrient export from each pixel based on nutrient sources on each LULC and the retention properties of the pixels belonging to the same flow path (Parn et al., 2012). The nutrient sources refer to nutrient applications across the LULC in the form of loadings and could be surface and subsurface sources (Hanshaw et al., 2009 ). The DEM raster map utilized as input for this model was identical to the one for the SDR model, downloaded from the SRTM database and processed using ArcGIS Pro 3.0.3. The LULC maps utilized for the scenario experiment were the pre and post fire land cover maps. The nutrient runoff proxy for this study was the annual precipitation downloaded from Daymet. The runoff proxy is used to evaluate the spatial variability of runoff which has the capacity to transport nutrient downstream. The biophysical table for this model was filled up with data from extensive literature search. For the prefire nutrient delivery modeling, the nitrogen and phosphorus loadings for each unique land cover type were obtained from the nutrient analysis report prepared by Tetra Tech ( 2015a ), Fenn et al. ( 2010 ), and nutrient database provided in the InVEST User guide (CITE). The retention efficiency eff, for each nutrient is the maximum nutrient retention expected from each LULC type. This ratio varies from 0 to 1, with high values (0.6–0.8) assigned to natural vegetation, indicating that 60–80% of nutrients are retained by these land cover (Sharp et al., 2020 ). The critical flow length which describes the distance of travel required to achieve the nutrient retention coefficient was set to the resolution of the input LULC raster map. The proportion subsurface n and Borselli K parameter values were obtained from the user guide of the InVEST NDR module (Sharp et al., 2020 ). For the postfire modeling, the nutrient loadings required as input of the NDR module were derived from published literature that focused on the contributions of wildfire on nutrient deposition. Based on the study of Koplitz et al. ( 2021 ) and Wright ( 1976 ), a 30% increase in Nitrogen loadings and about 38% increase in Phosphorus loadings were used to calibrate the post fire NDR biophysical table. Input data details and sources are provided in Table 1 . 2.4. Analysis of Wildfire Distributional Impacts to Communities After modeling the spatial distribution of regulating services before and after the fires, we additionally assessed the extent to which the Tubbs and Thomas fires led to changes in how ecosystem services and disservices are equitably or inequitably distributed across different communities, pre- and post-fire. We used Ordinary Least Squares (OLS) regression (Eq. 2) to investigate the extent to which several sociodemographic variables are associated with changes in water yield, soil loss, nitrogen loading, and phosphorus loading. Specifically, we overlayed the output rasters from each InVEST module on California Communities Environmental Health Screening Tool (CalEnviroScreen) US Census tracts (CEPAO, 2017). The spatial overlay allowed us to attribute InVest grid cells to US Census tracts. Each census tract had various InVest grid cells attributed to it. To estimate a tract-level quantity for each InVest variable, we took the mean of grid cells attributed to a particular census tract. As opposed to previous analyses described above, the data for the socio-demographic analysis were pooled together to ensure that the analysis has sufficient statistical power. $${y}_{i,h}={\beta }_{0}+{\beta }_{1}U+{\beta }_{2}P+{\beta }_{3}E+{\beta }_{4}H+{\beta }_{5}L+ϵ \left(2\right)$$ Where \({y}_{i,h}\) is the InVEST variable before the fire (subscript b ) for a given US Census tract (subscript i ). The explanatory variables of the regression include the unemployment rate, U , the poverty rate P , the Education level E , housing burden H , and the linguistic isolation, L . All explanatory variables are included as percentages. The definition of the unemployment rate, per OEHHA (2021), is: “Percent of the population over the age of 16 that is unemployed and eligible for the labor force” and poverty rate is defined as “Percent of population living below two times the federal poverty level”. The education level is defined as “Percent of population over 25 with less than a high school education” (OEHHA, 2021). Housing burden is defined as “Percent housing-burdened low-income households” (OEHHA, 2021). Finally, linguistic isolation is defined as “Percent limited English speaking households” (OEHHA, 2021). The error term is given by e and is assumed to be normally distributed and mean zero. The constant is β 0 , with the coefficients of the regression being the various β s. Next, we run a second OLS regression that examines the relationship of the sociodemographic variables with differences in the ecosystem services simulated by the InVEST before and after the fire, conditioning on the pre-fire levels of the ecosystem services simulated by InVEST. $${y}_{i,a}-{y}_{i,b}={\beta }_{0}+{\beta }_{1}U+{\beta }_{2}P+{\beta }_{3}E+{\beta }_{4}H+{\beta }_{5}L+{\beta }_{6}{y}_{i,b}+ϵ \left(3\right)$$ Where the variables in the above regression are the same as in Eq. 1, with the exception that the dependent variable is the difference before and after the fire \({y}_{i,a}-{y}_{i,b}\) and the InVEST variable before the fire is included as an explanatory variable \({y}_{i,b}\) . The effects of outliers are a larger concern for datasets with fewer observations. To limit the impact of outliers on the regression, we apply a hyperbolic arcsine transformation to all of the variables in equations 2 and 3. An added benefit of the hyperbolic arcsine transformation is that the interpretation of the coefficients in the regressions become approximations of percent changes (Bellemare and Wichman, 2020 ). Thus, the interpretation of any given coefficient from estimating equations (2) or (3) were that a 1 percent change in each sociodemographic variable on average results in a \(\beta\) percent change in either the pre-fire level of ecosystem service (Eq. 2) or the difference in pre- and post-fire ecosystem services, all in a given census tract (Eq. 3). For all statistical analyses we used the lfe software package (Gaure, 2013 ). 3.0. Results 3.1. Annual Water Yield The mean modeled annual water yield for the Pre-fire scenario in the Mark West subwatershed ranged between 168 mm and 945 mm, with a mean value of 699 mm. Post-fire land use map-based simulations show that the modeled mean annual water yield increased by 6% with a spatial range of 168 mm and 1032 mm, while the modeled spatial difference between the pre and postfire scenario ranges between 0 and 590 mm as shown in Fig. 2 . A decrease in modeled actual evapotranspiration was also observed for the post-fire modeling scenario compared to the pre-fire scenario. Similarly, an increase in modeled annual water yield was observed in the Southern California watersheds after the fire event. The spatial variation in the modeled water yield upon comparing the pre- and post-fire scenario ranges between 0 and 347 mm as shown in Fig. 2 . A difference of about 8.4 million m 3 was obtained in the modeled total annual water yield in the watershed, indicating a 42% increase in the modeled post fire annual water yield compared to the pre-fire scenario. The increase in modeled water yield could be attributed to the decrease in evapotranspiration and reduced interception due to changes in land cover. 3.2. Soil Loss and Sediment Delivery The land cover alteration due to the fire affects the soil loss in both watersheds. The modeled total soil loss showed about 66% increase in the Mark West subwatershed. An increase was also obtained in the Southern California sub-watersheds, where the modeled post-fire soil loss increased to 578,779 tons from about 567,335 tons before the fire event as shown in Table 2 . The modeled spatial difference between the pre and post-fire soil loss in the Mark West subwatershed ranges between 0 and 486 tons/ha/yr, and in Southern California ranges from 0 to 3,116 tons/ha/yr as shown in Fig. 3 . Table 2 Southern California (SoCal) watersheds ecosystem service modeling Category Prefire Postfire SoCal Annual Water Yield Water Yield (mm3/yr) 19,977,405 28,366,687 Water Consumption (mm3/yr) 80,687,266 101,133,453 SoCal Soil loss and Sediment Delivery Soil loss (tons) 567,335 578,779 Sediment Export (tons) 23,118 24,308 Sediment deposition (tons) 310,534 285,414 SoCal Nutrient Delivery Nitrogen loads (Kg) 99,219 122,646 Nitrogen export (Kg) 13,651 15,586 Phosphorus loads (Kg) 35,513 45,910 Phosphorus exports (Kg) 5,058 6,690 The same pattern was observed in the amount of modeled sediment exported from the land cover before and after the fire event. In the Mark West subwatershed, an increase of 49,961 tons of sediment was simulated in the post-fire SDR scenario, indicating a rise in sediment export induced by the fire event. The modeled spatial difference in sediment export for the Mark West subwatershed ranges between 0 and 94 tons/ha/yr, while that of Southern California sub-watershed extends from 0 to 139 tons/ha/yr as shown in Fig. 4 . 3.3. Carbon Storage and Sequestration The results for Total Above Ground Carbon and other categories are found in Table 3 . Both fires resulted in large losses in total above ground carbon. According to our modeling, the Tubbs fire removed 199,318.57 tons of carbon from the Mark West subwatershed. The Thomas Fire’s impact was smaller and across a relatively larger land area. It removed 158,662.41 tons of carbon from the South Ventura, Harmon Canyon, and Arrundell subwatersheds. Together, the two fires resulted in an emission of 1,312,597 tons of CO 2 equivalent carbon. For context, annual US GHG emissions are about 6 billion tons of CO 2 equivalent carbon. Table 3 Changes in total above ground carbon stores at the watershed level. Carbon stores Mark West Southern California Canopy -30,160.41 -52,545.93 Shrub -19,432.82 -12,1875.72 Herb -2,393.51 -294.13 Wood -67,473.64 -2,062.04 Litter Layer Mass -6,994.54 -81,812.24 Ground -71,878.7 -5,181.01 Total Above Ground -199,318.57 -158,662.41 3.4. Nutrient Delivery The mean modeled Nitrogen export in the Mark West subwatershed was 0.012 Kg/ha/yr before the fire and 0.014 Kg/ha/yr after the fire, a substantial increase. The modeled spatial difference in Nitrogen exports in this same watershed ranges between 0.001 Kg/ha/yr and 0.04 Kg/ha/yr as shown in Fig. 5 . Nitrogen exports in the southern California sub-watersheds showed similar trends as the Mark West counterparts, with increased nitrogen exports after the fire event. The modeled mean difference in the nitrogen export before and after the fire in the watershed in Southern California watershed was 0.18 Kg/ha/yr and ranged between 0.001 and 2.34 Kg/ha/yr spatially as shown in Fig. 5 . An 11% increase in modeled total Nitrogen export was observed in the Mark West subwatershed while about 14% increase was found in the Southern California watershed. Phosphorus exports in the watersheds also increased in both watersheds, according to our models. The total phosphorus exports in the southern and northern California watersheds before the fires were 5,058 Kg and 1,248 Kg, respectively. These values increased by 20% and 16% in the Southern California and Mark West watersheds respectively as shown in table 2 and table 4. The spatial difference in phosphorus exports ranges between 0 and 0.916 Kg/ha/yr in Southern California sub-watersheds and 0 and 0.013 Kg/ha/yr in the Mark West subwatershed as shown in Fig. 6 . Table 4 Mark West Sub-watershed ecosystem service modeling Category Pre-fire Post-fire Mark West Annual Water Yield Water Yield (mm3/yr) 103,335,478 108,045,478 Water Consumption (mm3/yr) 81,040,398 71,521,375 Mark West Soil loss and Sediment Delivery Soil loss (tons) 541,020 898,726 Sediment Export (tons) 44,332 94,293 Sediment deposition (tons) 377,916 612,338 Mark West Nutrient Delivery Nitrogen loads (Kg) 28,635 31,705 Nitrogen export (Kg) 4,758 5,297 Phosphorus loads (Kg) 7,652 8,785 Phosphorus exports(Kg) 1,248 1,447 3.5. Distributional Impacts of Wildfire to Communities Because the socio demographic analysis was performed on a pooled dataset containing observations in both watersheds, the presentation of the results differs from the previous results. Instead of presenting results for both the Mark West and Southern California watersheds, the results presented below are for both watersheds. The demographic variables included in both regressions did not have a substantial correlation with the distribution of the pre-fire InVEST outputs except in two distinct cases. First, census blocks that had higher levels of linguistic isolation were related to lower levels of annual pre-fire sediment loss, phosphorous loading, and nitrogen loading. Interestingly, linguistic isolation was not related with the pre-fire level of water runoff. Water runoff, on the other hand, was negatively associated with unemployment, poverty, housing burden, and education, all of which are statistically significant at the 5% level (Table 5 ; Column 1). Conversely, the amount of runoff was positively correlated with census blocks with higher education (Table 5 ; Column 1). It is important to note that several of the results might be driven by topographical features inherent to the communities (e.g. slope). We then estimated Eq. 3, which included the pre-fire levels of each InVEST variable to obtain Table 6 . Many of the differences between the InVEST variables before and after the fire were correlated with their pre-fire levels. For instance, the pre-fire levels of water yield were negatively correlated with changes in water yield following a fire ( \(\beta =-0.444\) ) such that the more pre-fire water yield there was, the lower the change due to wildfire was. Similarly, pre-fire levels of nitrogen and phosphorus loading were positively correlated with the change in nitrogen ( \(\beta =0.240\) ) and phosphorus ( \(\beta =0.306\) ) following a fire. Thus, the more pre-fire nitrogen and phosphorus there was, the higher the difference between pre- and post-fire levels of nitrogen. The pre-fire level of soil loss before the fire was not statistically significant in the regression. There were only three instances in which any of the socio-demographic variables were related to ecosystem service outcomes. Soil loss was positively correlated with housing burden. However, soil loss was negatively correlated with education. Further, housing burden was negatively correlated with nitrogen loading. Table 5 Regression results for estimating the impacts of sociodemographic variables on the pre-fire levels of water yield, sediment loads, phosphorus, and nitrogen. All variables are transformed using the hyperbolic arcsine transformation, and the coefficients can be regarded as approximations of percent changes. Sociodemographic variables Dependent variable : Pre-fire water yield Pre-fire soil loss Pre-fire phosphorus Pre-fire nitrogen (1) (2) (3) (4) Unemployment −0.141 ∗ 0.051 0.010 0.019 (0.078) (0.091) (0.006) (0.015) Poverty −1.038∗∗∗ −0.005 0.022 0.074 (0.325) (0.380) (0.027) (0.065) Housing Burden −0.370∗∗ −0.021 0.003 0.032 (0.158) (0.183) (0.012) (0.030) Education 0.370 ∗∗∗ −0.004 −0.008 −0.030 (0.108) (0.125) (0.008) (0.021) Linguistic Isolation −0.038 −0.140∗∗ −0.012∗∗∗ −0.024∗∗ (0.055) (0.064) (0.005) (0.011) Constant 9.206 ∗∗∗ 1.576 0.011 −0.039 (1.351) (1.577) (0.110) (0.269) Observations 63 63 60 60 R 2 0.247 0.087 0.169 0.166 Adjusted R 2 0.181 0.007 0.092 0.089 Note: *p < 0.1, **p < 0.05, ***p < 0.1 Table 6 Regression results estimating the impacts of sociodemographic variables on the change in ecosystem services following a fire for water yield, sediment load, phosphorus, and nitrogen. All variables are transformed using the hyperbolic arcsine transformation, and the coefficients can be regarded as approximations of percent changes. Dependent variable : Difference in water yield Difference in soil loss Difference in phosphorus Difference in nitrogen (1) (2) (3) (4) Pre-fire water yield −0.444 ∗ (0.256) Pre-fire sediment load 0.095 (0.120) Pre-fire phosphorus 0.306(0.026) Pre-fire nitrogen 0.240(0.033) Unemployment 0.094 0.042 0.0001 0.003 (0.156) (0.083) (0.001) (0.004) Poverty rate −0.193 0.515 −0.003 −0.011 (0.682) (0.345) (0.005) (0.016) Housing burden 0.478 0.284 ∗ −0.004 −0.013 ∗ (0.319) (0.166) (0.002) (0.007) Education −0.239 −0.219 ∗ 0.001 0.003 (0.230) (0.114) (0.002) (0.005) Linguistic isolation 0.021 −0.010 −0.001 −0.004 (0.107) (0.061) (0.001) (0.003) Constant 3.992 −2.215 0.014 0.054 (3.517) (1.444) (0.021) (0.066) Observation 63 63 60 60 R 2 0.142 0.123 0.771 0.567 Adjusted R 2 0.05 0.029 0.745 0.517 Note: *p < 0.1, **p < 0.05, ***p < 0.1 4.0. Discussion 4.1. Annual Water Yield The large increase in water yield occurring within the Southern California watersheds is consistent with the large and deadly debris flows occurring there following the Thomas Fire (Addison and Oomman, 2020). The differences in results between watersheds was likely driven by differences in landscape type. The Southern California watersheds are typically chaparral landscapes, which might have contributed to this difference, since chaparral landscapes might have particularly higher post-fire potential for increased runoff (Hubbert et al., 2006 ). One caveat is that while our analysis showed the change in annual water yield, the change in seasonal water yield might be different. For example, Adamowicz et al. ( 2019 ) showed that forests can act as a sponge and provide water during the dry season. Our results are consistent with that of different studies such as Saxe et al. ( 2018 ), Underwood et al. ( 2019 ) and Blount et al. ( 2020 ) who also show an increase in annual water yield because of a fire event and climate change. The rise in water yield observed in the Southern California watersheds is also consistent with those observed by Kinoshita and Hogue ( 2015 ) in their assessment of the impact of the 2003 Old Fire event on two ephemeral basins in Southern California. The mean actual evapotranspiration in the watershed was also reduced from 844 mm in the pre-fire scenario to 700 mm in the post-fire scenario. Vegetations such as trees and shrubs are often characterized by taking up a significant amount of water through their roots, losing this water to the atmosphere through transpiration and interception of water through canopy covers Turner ( 1991 ). However, in the event of forest fire, as in this study, vegetation destruction could lead to increased water yield in the watershed Basso et al. ( 2020 ); Pereira et al. ( 2016 ). In addition, reduced evaporation from soils and other surfaces could increase streamflow into rivers and potentially increase water yield (Kinoshita and Hogue, 2015 ). There is a considerable amount of research that has examined how wildfire impacts water yield. Results from this body of work suggest that impacts can be mixed. For instance, forest fire events may affect soil permeability by reducing soil compaction, allowing water infiltration into the soil, and increasing yield as baseflow (Gonzalez-Romero et al. (2021). Other work shows that wildfire can increase the repellency of soils, resulting in more water yield (Hubbert et al., 2006 ). Fire also affects soil hydraulics by depositing ash and sediments and developing water repellent CSIRO ( 2012 ), which could lead to increase in water yield Moody and Martin ( 2001b ). The long-lasting rise in water yield resulting from severe and widespread fires could modify the functioning of riparian ecosystems (Salemi et al., 2012). However, it may also offer a distinct possibility to supplement the regional water supply used by urban areas and agricultural communities. 4.2. Soil Loss and Sediment Delivery We also found an increase in the expected soil loss and erosion across both the Mark West subwatershed and the Southern California watersheds. While we provided the justification for the increase in soil loss and erosion based on the modeling approach, in practice, the increase in soil loss observed in the post-fire scenario could have also been due to the loss of organic matter and hydrophobicity associated with the burning of land cover. Fires can reduce the ability of the soil to hold moisture by burning organic matter, making the soil more erodible (Shakesby and Doerr, 2006 ). Soil structure disruption associated with intense fire heat increases soil compaction and reduces infiltration capacity, making the soil vulnerable to erosion. Post-fire activities also lead to increased surface runoff and high flow with the potential of detaching and transporting significant soil particles. Ash deposition upon burning of land cover can lead to a hydrophobic layer on the soil, reducing water infiltration into the soil, which could result in high surface runoff and favor the removal of soil particles (Larsen et al. 2009 ). Alteration of channel morphology, such as increased channel roughness and formation of sediment deposits after a fire event associated with burnt land cover, could also enable higher sediment export (Moody and Martin, 2001b ). Fires have a vast impact on channel morphology which influences sediment availability. Channels provide sediment through the upstream extension of head cuts, lateral bank erosion, and further destruction of banks (Moody and Martin, 2009 ). Erosion rates and sediment yield have been widely documented to increase after fire events, often due to the direct and indirect effects of the burning (Moody and Martin ( 2001a ); Robichaud et al. ( 2020 ); Biswas et al. ( 2021 )). Other studies also report an increase in sediment yields as a result of wildfires. For example, East et al. ( 2021 ) showed that post-fire sediment yield in Whiskeytown National Recreation Area in Northern California increased after the Carr fire incident. In another study, using a physically based model Rulli and Rosso ( 2005 ) find a substantial increase in erosion and sediment in nine basins in Southern California due to a series of wildfires. Finally, Coombs and Melack ( 2013 ) found an increase in suspended sediment export from 82% burnt San Onofre watersheds characterized by chaparral vegetation in Southern California compared to similar unburnt watersheds. The rise in soil loss and sediment export could be related to vegetation loss induced by fire events Parise and Cannon ( 2012 ). This is caused by the loss of plant roots and above-ground biomass, which removes the protective cover that holds the soil and makes it susceptible to gradual soil loss. Forest and vegetation covers are important for binding soil particles together using a network of fibers in their roots which increases soil stability and prevents erosion Parise and Cannon ( 2012 ). The loss of above-ground biomass, such as leaves, stems, and branches, exposes the soil to the direct impact of rainfall and wind, which triggers the loss of soil particles. The loss of canopy cover in forested watersheds due to fire events reduces rainfall interception and increases the wind effect on the soil. Hanshaw et al. ( 2009 ) observed that rainfall amount measured by a rain gauge beneath a chaparral shrub canopy was reduced to about 42% than those observed in adjacent burnt areas. These actions in isolation or combination can increase the pressure on the soil surface and their erosive potential. Furthermore, vegetation loss due to wildfire could reduce the stabilization of slopes and increase soil erosion along the slopes due to landslide mass movement and increase the rate of sediment exportation (McEachran et al., 2021 ). 4.3. Carbon Storage and Sequestration The differences in the carbon losses reflect the differences in the size of the watersheds and the differences in vegetative land cover across regions. For instance, while the Mark West lost 19,432.82 tons of carbon in the shrub category, the Southern California Watersheds lost 121,875.72. This represents approximately 0.3% of the 45 teragrams of total carbon storage in the Southern California National Forests reported by Underwood et al. ( 2019 ). Similarly, the Southern California Watersheds lost far more carbon in the litter Layer Mass (LLM) category. The Mark West subwatershed lost substantially more carbon in the wood category (67,473.64 tons) than the southern California sub watersheds (2,062 tons). Interestingly, though the Mark West subwatershed lost 30,160 tons of carbon in the Canopy category, the three Southern California watersheds gained 52,545 tons in the year following the fire. This is likely due to the pattern of landscape changes that occur following a fire for fire-adapted chaparral ecosystems (Storey et al., 2021 ). 4.4. Nutrient Delivery Our analysis showed an increase in the expected post-fire phosphorus and nitrogen export. Increased phosphorus and nitrogen exports in the watersheds could be detrimental to downstream waterbodies by providing conditions that favors the rapid growth of algal and other invasive species. Comparing the nutrient exports with other studies in different study areas could be complex due to varying rainfall amounts triggering runoff generation, the variation in fire severity, post-fire activities, ecosystem characteristics that drive nutrient mobility, and variation in methods used for nutrient measurements (Lane et al. 2008 ); Smith et al. 2011 );. However, studies such as Ferreira et al. ( 2005 ); Coombs and Melack ( 2013 ); Goodridge et al. ( 2018 ) also showed an increase in nutrient exports after a fire event. Coombs and Melack ( 2013 ) specifically observed increased dissolved organic nitrogen (DON) and phosphorus exports in a burnt chaparral-dominated watershed in Southern California triggered by a high storm event and additions of Nitrogen in the form of ammonium and DON deposited as ash on the soil surface. Increased nitrogen and phosphorus exports in burnt watersheds can be related to the loss of vegetation cover (Rodríguez-Romero et al. 2018 ). Vegetation cover plays a vital role in the uptake of nutrients from the soil. Their loss could lead to decreased nutrient retaining capacity and increased availability of these nutrients for export. Soil disturbance associated with burnt landscapes interferes with erosion intensity and soil compaction, which disrupts natural nutrient cycling and could lead to increased availability of Nitrogen and phosphorus to be exported Smith et al. ( 2011 ). The enhanced export of Nitrogen and phosphorus from an ecosystem can also be considerably influenced by the ash deposition after a fire (Goforth et al., 2005 ). Ash introduces a variety of organic and inorganic components, such as Nitrogen and phosphorus, when it falls on the soil’s surface. As a result, many mechanisms that improve nutrient export are implemented (Reneau et al. 2007 ). Nutrients are easily freed from ashes, making them easier to distribute; however, InVEST does not consider this effect. On a regional basis, the increase in postfire nitrogen and phosphorus export observed in this study aligns with the observed trend in the western United States, often associated with elevated phosphorus and nitrogen levels during the first five years after a fire event (Rust et al. 2018 ). These increases in nutrient exports have stringent implications on water quality and management strategies. Nitrogen and Phosphorus are often limiting nutrients for algal growth in freshwater and coastal systems. Their high occurrence is a recipe for eutrophication, potentially causing toxic algal blooms. 4.5. Analysis of Distributional Impacts of Wildfire to Communities The sociodemographic analysis found that differences in modeled water yield, soil loss, phosphorus, and nitrogen levels before and after wildfire were largely not correlated with sociodemographic variables. Rather, the pre-fire levels of these variables were correlated with sociodemographic variables. The implication of these results is that although ecosystem services are inequitably distributed across the landscape, wildfires neither exacerbated nor improved the distribution of ecosystem services, according to our modeling. It is important to note that this result might be specific to the collection of ecosystem services considered in this study, and to the study areas considered. When estimating Eq. (2), several relationships were found between ecosystem service provision and sociodemographic variables. For instance, a 1% increase in the poverty rate of a census tract was correlated with a 1.038% decline in water yield, such that the higher the poverty rate of a census tract, the less water was passing through the census tract. This observation was also true of housing burden, where a 1% increase in housing burden was correlated with a 0.37% decline in runoff, all else constant. Recent work has examined linkages to water and poverty levels. For instance, Alqatarneh and Al-Zboon ( 2022 ) create a water poverty index for resource management in Jordan. In the United States, Deitz and Meehan ( 2019 ) investigated hot spots for water inequality along racial and geographic lines, finding clear relationships between the quality of plumbing and race. The results of our study support findings in Deitz and Meehan ( 2019 ) in that we too find that, even in wildland systems, there is distributional inequality in water resources. This highlights the need for tools like those presented in Alqatarneh and Al-Zboon ( 2022 ) for helping to relieve inequalities. We also found that linguistic isolation is negatively correlated with soil loss, such that a 1% increase in linguistic isolation is correlated to a 0.14% decline in soil loss. There are similar relationships between linguistic isolation and phosphorus and nitrogen loading; however, the magnitudes of the changes are exceptionally small. Our results indicated that pre-fire levels of soil loss are lower in communities with fewer English speakers. Many studies look at soil pollution, rather than soil loss. For instance, Masri et al., ( 2020 ) looks at soil lead distributions in the city of Santa Ana California, finding that soil lead was much higher in socioeconomically disadvantaged neighborhoods. Though many studies have addressed how wildfires impact the provision of wildfires (Lee et al., 2015 ; Vukomonovic and Steelman, 2019; Pereira et al., 2021 ), to the best of our knowledge there is no other study that addresses the distributional impacts of wildfires on ecosystem services. However, the distributional equity of ecosystem service supply has been a well-studied issue. Much of the work on ecosystem services and environmental justice has been done in urban landscapes (e.g. Geneletti et al., 2020 ) with others expanding analysis to the WUI (e.g., Thomas et al., 2022 ; D’Evelyn t al., 2022). Our results showed that the inequality in the distribution of water, soil loss, nitrogen, and phosphorus loading are mainly driven by pre-existing inequalities that were present prior to wildfire. There are many other dimensions of inequality related to wildfire, including who is impacted by home loss and smoke (Thomas et al., 2022 ). Future work might benefit from comparing inequality before and after fires and other large natural disasters to see how these events impact different kinds of inequality. Such work will be vital for identifying changes to policy needed to alleviate inequality. A notable observation in these results is the lack of evidence for sociodemographic variables being correlated with either pre-fire ecosystem service levels or with differences in ecosystem service levels before and after fires. There is an ample literature which seeks to illustrate how sociodemographic groups might sort into areas of differing environmental quality (e.g. Martín-López et al., 2012 ; Faccioli et al., 2020 ). However, one way to interpret our findings is that we do not find evidence to support this literature, at least in the landscapes which we included in this study. 5.0. Conclusion This study assessed the effects of wildfires on annual water yield, carbon sequestration, soil loss, sediment exports, phosphorus delivery, and nitrogen delivery. We found that the magnitude of changes amongst each ecosystem service vary greatly between the watersheds studied. Increased annual water yield was observed for both Mark West and Southern California watersheds after wildfire event. We observed that after the wildfire event, about 11% and 14% nitrogen export increase was found in the Mark West and Southern California watersheds respectively. A 16% increase in phosphorus export was also observed in the Mark West sub-watershed compared to the 32% increase observed in the Southern California watersheds. The differences and similarities in how fire affected each region is important for post-fire management. Importantly, our modeling results were consistent with finding in Hubbert et al. ( 2006 ) in that chaparral landscapes have particularly high potential for runoff events following fire compared with non-chapparal landscapes; even leading to disservices in the form of floods. Consistent with Hubbert et al. ( 2006 ), even though the change in post fire runoff is large, changes in sedimentation, nitrogen, and phosphorus levels of the water are not high. There are several important limitations and avenues for future research. A relevant limitation of the annual water yield model for this study is the lack of spatial distribution of land cover such that complex land use changes are not adequately characterized. This limitation means that complex changes in land use may not be comprehensively accounted for in model predictions (Sharp et al., 2020 . The InVEST model was selected due to its flexibility, interpretability, and reproducibility; however, other modeling frameworks might provide more accurate results for focused studies, such as Soil and Water Assessment Tool (SWAT). Further, there are a wide variety of other ecosystem service bundles that could be considered. We limited our analysis to carbon, water quantity, and water quality due to the salience and importance of these services in the California context; however, air pollution, recreation, and ecosystem disservices are also important to consider by future research. Past studies on the topic have also considered the economic values of these ecosystem services (e.g. Underwood et al., 2019 ). Future work can combine flexible benefit transfer methodologies with modeling output to achieve estimates of economic damage resulting from the fire. However, this might be challenging, especially for water resources, since the per-unit values of water resources might be dependent on the volume. Although our study did not account for the ability of different communities to adapt to changes, nor did the distributional impacts to ecosystem services did not change, procedural justice and economic inequalities might still result in an inequal distribution of benefits and even disservices to certain communities. Future work can benefit from incorporating the community level adaptive capacity into post-wildfire recovery. The relationship between natural disasters and the provision of ecosystem services is a long-studied topic that still commands attention from various disciplines. As wildfires continue to affect human communities more, understanding their impacts will become more necessary. 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Sloggy","email":"","orcid":"","institution":"USDA Pacific Southwest Research Station","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"R.","lastName":"Sloggy","suffix":""},{"id":285891331,"identity":"c941e10d-5200-4049-b247-1b97baf2c82b","order_by":2,"name":"Mani Rouhi Rad","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Mani","middleName":"Rouhi","lastName":"Rad","suffix":""},{"id":285891332,"identity":"d3642990-c624-4f5b-bf88-1fd9c0b42d1e","order_by":3,"name":"Debabrata Sahoo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDCCAwxsQNKGgQ9JhCgtaWCSJC2HSdDCd/7wswcfKs7Ls7F3J3/4uINBju9GAn4tkjfSzA1nnLlt2MZzdpvkzDMMxpKEtBjcYDCT5m27ncAmkbuNmbeNIXEDQS3nj38DajmXwCb/dvNnoJZ6wloO5IBsOQC0hXcDkMGQYEDYLzllkjPOJAP9kgv0S5uE4cwzD/Br4Tt/fJvEhwo7eX72s5s/fGyzkec7TsAWdCBBmvJRMApGwSgYBdgBANEtRYlPU93yAAAAAElFTkSuQmCC","orcid":"","institution":"Clemson University","correspondingAuthor":true,"prefix":"","firstName":"Debabrata","middleName":"","lastName":"Sahoo","suffix":""},{"id":285891333,"identity":"453b15c4-ca26-4ef9-9930-473321b813d8","order_by":4,"name":"Stacy A Drury","email":"","orcid":"","institution":"USDA Pacific Southwest Research Station","correspondingAuthor":false,"prefix":"","firstName":"Stacy","middleName":"A","lastName":"Drury","suffix":""},{"id":285891334,"identity":"a7329bda-5b02-4d53-a78d-b70e84cddd72","order_by":5,"name":"Francisco J. Escobedo","email":"","orcid":"","institution":"USDA Pacific Southwest Research Station","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"J.","lastName":"Escobedo","suffix":""}],"badges":[],"createdAt":"2024-03-29 19:29:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4189499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4189499/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00267-025-02185-3","type":"published","date":"2025-05-23T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54038232,"identity":"1169e44d-0d7f-4271-ad1e-03c8570c085e","added_by":"auto","created_at":"2024-04-03 17:14:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129757,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4189499/v1/c78d8709894cf6aecfce2f3b.jpg"},{"id":54038234,"identity":"b6c5ccc5-9690-44a6-beaa-252f8f8ac7a1","added_by":"auto","created_at":"2024-04-03 17:14:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1255875,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure shows the difference in annual water yield before and after the fire in the Mark West (left) and southern California watersheds (right).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4189499/v1/ea1e9fcb03206ebaa866f549.png"},{"id":54038236,"identity":"7edfb3c7-61c8-494d-b6a5-6f5a4217d6a5","added_by":"auto","created_at":"2024-04-03 17:14:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1517494,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure shows the difference in soil loss before and after the fire in the Mark West (left) and southern California watersheds (right).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4189499/v1/5727d576af5956e5d935979f.png"},{"id":54038235,"identity":"fa443250-712c-447a-bd1c-f1e14064a174","added_by":"auto","created_at":"2024-04-03 17:14:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1465529,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure shows the difference in sediment export before and after the fire in the Mark West (left) and southern California (right) watersheds.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4189499/v1/5a6882c672d10c1f37e01a1a.png"},{"id":54038231,"identity":"2614577b-7022-45e9-ab1c-c05a481a7276","added_by":"auto","created_at":"2024-04-03 17:14:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1879577,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure shows the difference in nitrogen export before and after the fire in the Mark West (left) and southern California watersheds (right).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4189499/v1/3513c4693d8d846f77421597.png"},{"id":54039650,"identity":"23e2d6b2-cafe-4e87-bced-06e37e151c8f","added_by":"auto","created_at":"2024-04-03 17:22:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1870982,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure shows the difference in phosphorus export before and after the fire in the Mark West (left) and southern California watersheds (right).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4189499/v1/13e41451b502fb3173555035.png"},{"id":83460756,"identity":"aeaeb528-3341-4061-89d0-8f1d0fc83459","added_by":"auto","created_at":"2025-05-26 16:13:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8492697,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4189499/v1/2290429a-bd3e-41ae-a6f9-253e9c2230b3.pdf"},{"id":54038233,"identity":"236c92dd-c505-4101-b8cb-3e6291d09021","added_by":"auto","created_at":"2024-04-03 17:14:40","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":39081,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryEcosystemService32724.docx","url":"https://assets-eu.researchsquare.com/files/rs-4189499/v1/8ee46c40f75b9d419c1005c8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Wildfire effects on ecosystem services in two disparate California watersheds: A Case Study","fulltext":[{"header":"1.0. Introduction","content":"\u003cp\u003eWatershed ecosystems provide a variety of ecosystem services including carbon sequestration, water quantity and quality improvement, and regulation of pollution levels (Brockerhoff et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These ecosystem services are essential for maintaining life and ensuring management and environmental quality goals. A major threat to of the sustainable supply of ecosystem services is the increased frequency and severity of wildfires due to land use and climate change, in more arid regions of the Western US (Westerling et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), Australia (Haque et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Western and Southern Europe (Dupuy et al., 2021) and South America (Ciocca et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While wildfires are part of many properly functioning ecosystems (Lecina-Diaz et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), changing wildfire regimes will likely result in changing levels of ecosystem service provision on severely and frequently fire-affected watersheds (Pereira et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis paper studies the effects of wildfires on the regulating ecosystem services from Forest along with the change in the distributional impacts of ecosystem services across local communities. The study area includes two fire-affected landscapes: the Mark-West subwatershed before and after the Tubbs fire, and Harmon Canyon, Arundell, and Ventura subwatersheds in Ventura and Santa Barbara County which were impacted by the Thomas Fire. The diversity in watersheds provided by these two areas makes them useful for this topic. In addition, California has experienced an increase in the frequency and severity of wildfires throughout the state partly to climate change as well as human-driven factors (Westerling and Bryant, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Under climate change, the state may observe further increases in wildfire risk in the future (Westerling and Bryant, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This makes it an ideal and important area to consider when studying how fires might impact the equitable and sustainable distributions of ecosystem services.\u003c/p\u003e \u003cp\u003ePost wildfire events lead to increased soil loss and sediment export including water quality degradation, reduction of nutrient-enriched soil, habitat destruction, reduced soil productivity, and water scarcity (CITE). Soil loss and transportation into rivers, lakes, and reservoirs compromise water quality by increasing turbidity and introducing pollutants, which could contaminate the water (Issaka and Ashraf \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Soil erosion can also lead to the washing away of essential topsoil, which is enriched with nutrients, reducing soil fertility, and impacting plant growth and productivity (Orgiazzi and Panagos, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This can result in a significant loss of agricultural yield and affect food and resource availability. Sedimentation of receiving rivers, reservoirs, ponds, and channels is often attributed to increased soil loss from the watershed, and sediment export reduces the water carrying capacity of the waterbody and causes overflowing off their banks, leading to flooding (Uri, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Soil erosion also threatens terrestrial biodiversity by impacting communities of the fauna inhabiting the soil through habitat degradation (Guerra et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which could affect ecosystem functioning.\u003c/p\u003e \u003cp\u003eWe contribute to the literature on the effects of wildfires in two ways. First, several studies have analyzed the effects of fires on ecosystem services provided by forested watersheds. Lecina-Diaz et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) studied the risk to forest-based ecosystem services based on a hazard index that depends on the availability of exposed ecosystems from the forests, the probability of fire hazard from weather, and the capacity of the forest to recover after a fire. Lee et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) studied the benefits of climate change mitigation by studying their benefits on reducing the loss of ecosystem services from forests. They used a habitat equivalency analysis that estimates the loss of ecosystem services as acre-years of lost vegetation and considered the avoided cost of fuel treatment as benefit of mitigation. We contribute to this literature by studying the changes in regulating ecosystem services provided by forests across watersheds in two different regions of California.\u003c/p\u003e \u003cp\u003eSecond, though several studies have examined how ecosystem services are distributed across human settlements (Plieninger 2013; Fu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and many have addressed the effects of wildfire on ecosystem services (e.g., Vukomanovic and Steelman, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), there remains a gap in examining how wildfires change how ecosystem services are spatially and equitable distributed across different communities in a watershed (Yadav et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Core to this paper and many others examining environmental justice are the concepts of distributional impacts and community well-being Thomas et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, we examine specifically the distribution of ecosystem services, which are defined as benefits received by individuals that flow from environmental sources (Chen et al., 2023). The ecosystem services are distributed spatially across the landscape, which influences how they are distributed across different sociodemographic groups. In addition to ecosystem services, the landscape can also produce ecosystem disservices, which are defined as costs incurred by individuals as opposed to benefits (Escobedo et al., 2011). Wildfire changes the spatial distribution of the ecosystem services, and thus changes the distributional impacts of these as well.\u003c/p\u003e \u003cp\u003eThis aim of this paper was to model the spatiotemporal and distributional impacts of wildfires on watershed-scale ecosystem services across different communities. The specific objective of this study is to 1) assess the effects of two wildfires in different ecoregions and watersheds in California had on: i. water quantity, ii. soil loss and sediment delivery, iii. carbon sequestration, iv. nutrient delivery and 2) understand the pre- and post-fire spatial and distributional impacts to ES supply across different sociodemographic groups. The main contribution of this study is the novel linkage of fire and ecosystem service modeling to sociodemographic analysis to estimate the distributional impacts of wildfires on ecosystem services.\u003c/p\u003e"},{"header":"2.0. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eOur study focuses on the Mark West subwatershed in northern California, and the Harmon, Arundel and Ventura subwatersheds in southern California hereafter called Southern California watershed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These watersheds were chosen because they were both subject to large wildfires that greatly impacted the area and the two watersheds are in two different ecoregions. In addition, the differences in size, climate, land cover, and other physical attributes between these two watersheds can be used to better understand the heterogeneity in the effects of wildfires on ecosystem services. For example, the Mark West subwatershed is considerably smaller (14,767 hectares) than the Southern California watershed (26,379 hectares).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Mark West subwatershed is at an altitude of between 5 meters and 850 meters above sea level (Woolfenden et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The watershed of the Mark West Creek has a mediterranean climate and is part of a larger region that is characterized by several features including oak woodlands, grasslands, and riparian woodlands (Potter and Hiatt, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) The Southern California watershed is coastal and has a maximum altitude of 1833 meters at its headwaters and has a Mediterranean climate and characterized by large amounts of chaparral vegetation (Jumps et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Mark West subwatershed was burned during the 2017 Tubbs fire. The Tubbs fire burned through parts of Sonoma County, entering many populated areas including Santa Rosa, Sonoma county\u0026rsquo;s largest city (Cortenbach et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The fire destroyed over 5,643 structures and 22 people lost their lives (LeComte, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The Southern California watershed also burned in 2017 during the Thomas fire. The fire burned through parts of Ventura and Santa Barbara counties (Kolden and Henson, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and resulted in a large landslide that destroyed parts of the highway 101 south of Santa Barbara (Lukashov et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The fire and mudslide that followed took the lives of 23 people (Kress, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). More detailed accounts and descriptions of both fires are found in supplementary file.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Fire Effects Modeling Process\u003c/h2\u003e \u003cp\u003eThe fire effects modeling process was designed to provide pre- and post-burn above ground biomass data for the 2017 Tubbs fire in northern California and the 2017 Thomas Fire in southern California. Both fires started outside the wildland fire interface (WUI) and burned through the WUI into urban areas in Santa Rosa Ca and Ventura CA respectively.\u003c/p\u003e \u003cp\u003eIn this process, fire perimeter data is downloaded from the monitoring trends in burn severity project (MTBS; Eidenshink 2007). Pre-burn above ground biomass is estimated using the fuel characteristic classification system maps (Fuel Characteristic Classification System; Ottmar et al. 2007, Prichard et al. 2013) as provided by the LANDFIRE project data distribution site (Rollins 2009). Initial fire severity observations are downloaded from the Rapid Assessment of Vegetation Condition (RAVG; less than 1-month post-fire; Miller and Thode 2007, Miller and Quayle 2015) followed by downloading 1-year post-fire observations from MTBS (Eidenshink 2007). Post-burn above ground biomass is estimated based on biomass reduction equations using the fire severity observations (Prichard et al 2017) and the fire and fuels tools software package (Prichard et al 2013).\u003c/p\u003e \u003cp\u003eSpecifically, we used the fire perimeters created by MTBS to define the area affected by fire. The MTBS project uses Landsat earth observations taken of the general fire area before the fire and then after the fire in combination with fire perimeters gathered by GIS specialists to determine the fire perimeter which becomes the final perimeter of record. The study area was further defined to the areas covered by the Mark West Creek watershed for the Tubbs Fire and the Lower Ventura River, Arundell Barranca-Frontal Pacific, and the Harmon Canyon-Santa Clara River watersheds for the Thomas Fire from the California HUC12 watershed delineation maps. Each of these watersheds contained significant portions of urban, wildland urban interface (WUI) landscapes in the watershed that were burned by wildfire.\u003c/p\u003e \u003cp\u003ePre-fire vegetation type was estimated for each 30 square meter pixel within the watershed boundaries using the Fuel Characteristic Classification System (FCCS) layer included in the 2016 release of the LANDFIRE fuels and vegetation layers (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.landfire.gov\" target=\"_blank\"\u003ewww.landfire.gov\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.landfire.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the LANDFIRE existing vegetation layer (EVT: Rollins 2009). The LANDFIRE EVT and the LANDFIRE FCCS layers each contain vegetation type descriptions that progress from generic coarse scale vegetation type descriptions such as \u0026ldquo;shrubland\u0026rdquo;, \u0026ldquo;conifer\u0026rdquo; or \u0026ldquo;hardwood\u0026rdquo; to fine scaled descriptive names including \u0026ldquo;California Coastal Live Oak Woodland and Savanna\u0026rdquo;. To simplify the analysis, we used the more generic coarse scale descriptions since few pixels in our landscapes contained the more specific vegetation types. Above ground biomass was estimated using the LANDFIRE FCCS layer and associated database (Pritchard et al. 2017). FCCS provides biomass estimates which can be summed into the following broad categories: tree, shrub, herbaceous, downed and dead logs, and forest floor biomass (new and decomposed biomass). FCCS provides biomass estimates in biomass per unit area such as tons per acre (Ottmar et al. 2007). We used ArcMap 10.5 and excel spreadsheets to link the EVT maps with the FCCS maps to produce the pre-burn estimates by vegetation type for each of our two large watersheds.\u003c/p\u003e \u003cp\u003eBiomass changes across the fire affected landscapes in the watersheds were estimated using fire severity metrics provided by MTBS (Eidenshink et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Fire severity is estimated by quantifying vegetation reflectance differences where Landsat imagery is compared before and after fire using the relative differenced Normalized Burn Ratio (RdNBR; Eidenshink et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The quantified difference is then related back to the original vegetation to estimate changes in vegetation condition, status (live or dead), and biomass consumed (Drury et al. 2014, Prichard et al. 2017).\u003c/p\u003e \u003cp\u003eThis methodology does not enable us to determine vegetation type changes but does provide tools to estimate biomass remaining on the landscape after burning (Drury et al. 2014, Prichard et al. 2017). Specifically, we combined the fire severity maps with post-fire biomass remaining calculations produced by the FCCS Fire and Fuels Tools (Ottmar et al. 2009) and post-fire biomass equations developed for the LANDFIRE mapping project (Prichard et al. 2017) using ArcGIS 10.5 to produce a custom set of biomasses remaining in the fire affected areas of the Tubbs and Thomas fire perimeters. The resulting pre- and post-burn biomass maps serve as inputs into the INVEST model described below.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Ecosystem Services Modelling\u003c/h2\u003e \u003cp\u003eWe used four different InVEST modules to model five different ecosystem services that are affected by wildfires: 1) carbon storage, 2) carbon sequestration, 3) annual water yield, 4) sediment delivery, and 5) nutrient delivery (phosphorus and nitrogen). The InVEST model have previously simulated the effect of climate change and land use land cover change on ecosystem services like water yield and supply (Clerici et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Changes in each ecosystem service in this study were estimated by comparing modeled ecosystem services before and after fire for each watershed. Land cover alterations that occur after the fire event describe the impact of the fire in each watershed on existing vegetation cover. We first estimated the outcome of each ecosystem process using the vegetation cover prior to the fire for each watershed. We then estimated the ecosystem outcomes for the vegetation cover one year after the fire. The difference between the two provided us with the change in ecosystem services as a result of the fires. The reason that we selected one year after each fire as our post-fire ecosystem service valuation is that vegetation changes are more stable a year after a fire takes place than considering day-to-day changes in vegetation and ecosystem services immediately after a fire, which may not provide an accurate representation of ecosystem service changes. We calculated the differences between the InVEST model outputs prior to and following the fire by loading both outputs as rasters into R (R Core Team, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and using the raster package (Hijmans, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to take the difference in values between the two rasters. Since the InVEST modules requires a set of different inputs so we parameterized each module using a variety of inputs listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Annual Water Yield\u003c/h2\u003e \u003cp\u003eThe annual water yield module of InVEST quantifies the contribution of different parts of the watershed to the overall water reaching the outlet in a year (Wu et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This refers to all forms of water movement that originate from precipitation, snowmelt, and other sources in the watershed. The annual water yield of a watershed is an essential ecosystem service that supports human life and development. The InVEST annual water yield module estimates the water yield for a watershed at the pixel-level and at the watershed-level water (Sharp et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This module can also estimate the economic value of energy produced using the water supplied to the hydropower reservoir based on the contributions of water runoff from each landscape type (Sharp et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our analysis excludes the hydropower economic valuation component of the model because hydropower is not a major electricity producer in this region. Generally, the model determines the quantity of water yield per pixel as the difference between precipitation and evapotranspiration (ET). In this study, we estimate the change in the annual water yield of each watershed due to burning by using the InVEST annual water yield module to first estimate water runoff from each pixel and aggregate runoff for each watershed before and after the relevant fire event. Then, the difference between annual yield before and after a fire is presented as the effect of wildfires on the change in annual water yield.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInput data details\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInput\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnits\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall Erosivity index (R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlobal Rainfall Erosivity Database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMJ.mm.(ha.h.yr)-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Erodibility (K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003egSSURGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et.ha.hr.(MJ.mm.ha)-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Elevation Model (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDaymet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand management factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiterature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnitless\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCover factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiterature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnitless\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant Available Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003egSSURGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference Evapotranspiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDaymet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth To Root Restricting Layer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003egSSURGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorselli k Parameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVigiak et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnitless\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorselli IC0 Parameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVigiak et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnitless\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreshold flow accumulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVigiak et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnitless\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax SDR Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax SDR Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVigiak et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnitless\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSuccessful estimation of the annual water yield module requires input data on precipitation, biophysical information, evapotranspiration, plant available water content, root restricting layer depth, and consumptive water use. Precipitation data was obtained from DayMet (Thornton et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for the period of 1987 to 2017 based on the tile numbers that represent the study area. Daymet is a data-driven product that uses various algorithms for interpolation and extrapolation of daily meteorological parameters to produce gridded daily parameters at a spatial resolution of 1 km. Daily precipitation values in millimeters were summed by year and then cropped to the study watershed areas to obtain the average annual precipitation estimates. The minimum and maximum temperature and solar radiation were also downloaded from the same database and used for the estimation of the reference evapotranspiration and cover l(ETo) based on the modified Hargreaves equation. These meteorological variables used as model input are historical average across the 30 years of data acquisition. Data on Depth to Root Restricting Layer was obtained from the Gridded Soil Survey Geographic (gSSURGO) database. Similarly, Plant Available Water Content Fraction which is the ratio of actual ET and precipitation was obtained from the gSSURGO database. Watershed shapefiles were secured from the U.S. Geological Survey\u0026rsquo;s Watershed Boundary dataset while the same land use /land cover details (as in the case of the carbon model) were applied to the annual water yield model estimation.\u003c/p\u003e \u003cp\u003eAdditional data efforts focused on creating biophysical parameters and water demand information relevant to the water yield model estimation. Accumulated biophysical data includes land use/land cover codes for each landscape class, crop coefficients (Kc values), root depth, and Z-parameter. Land use/land cover codes are integers and remain the same as those used in the carbon model. Kc values for each land cover classification were secured from NISTOR et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Root depth for each land cover type was also obtained from published studies (Canadell et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Maximum root depth for each vegetation type measures the depth to which at least 95 percent of root biomass occurs. Finally, we calculated the Z-parameter using omega estimates based on the work of Xu et al. (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and the mean of precipitation and available water content earlier described.\u003c/p\u003e \u003cp\u003eWe focused on the Mark West subwatershed and the Arundel, Ventura and Harmon sub-watersheds representing the Southern California watersheds, estimating water yield, consumption, and scarcity before and after fire events.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Sediment Delivery\u003c/h2\u003e \u003cp\u003eBy altering vegetation cover and litter, fires can also change the amount of sediment exported from a catchment (Warrick et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The sediment delivery ratio module of the InVEST model was used to assess the annual sediment exported from the catchment to the outlet. The model uses a combination of the soil loss calculated through the revised universal soil loss equation (RUSLE) and the sediment delivery ratio (SDR), which quantifies the proportion of soil loss reaching the outlet. The model works explicitly on the spatial resolution of the digital elevation model (DEM) and performs its operation for each pixel (Sharp et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The SDR estimation begins by computing the hydrological linkage between sediment sources and streams, often called connectivity index (IC), which is a function of the area upslope of each pixel and the flow path between the pixel and the nearest stream.\u003c/p\u003e \u003cp\u003eThe data sources for the InVEST SDR model ranges from literature, organizations, public reports and agencies. The rainfall erosivity index, (R hereafter), quantifies the intensity of rainfall to initiate soil loss and was obtained from the global erosivity map published by the Joint Research Centre of the European Commission. The map was a result of an extensive project focused on estimating rainfall erosivity across 63 countries (Panagos et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The soil erodibility factor (K) was derived from the United States Department of Agriculture\u0026rsquo;s NRCS gSSURGO database and measures the ability of the soil to be eroded under standard condition. The R and K factor for the study sites were clipped out of the raster map obtained from their respective databases. The Digital Elevation Model (DEM) raster file was obtained from the Shuttle Radar Topography Mission (SRTM) database and processed using ArcGIS Pro 3.0.3. The Fill Sink tool in the software was used to fill the depressions in the DEM. The threshold flow accumulation and Borselli K parameter were set at 1000 and 2 respectively. The cover management factor (C), utilized in this study quantifies the ability of a land use type to resist erosion. This value ranges from 0 to 1, with values closer to 0 indicating that less erosion is likely to occur while values closer to 1 indicate more is likely to occur in the land use pixel. The C-factor for the pre-fire land cover map for the watersheds was obtained from ensemble sources including literature, sediment database provided in the InVEST User guide, and technical reports such as Tetra Tech (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e) and McKague (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The C-factor for post-fire land cover map was however derived based on results from published literature (Terranova et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), which assigns a C-factor to land cover based on burn severity. This study assigns C\u0026thinsp;=\u0026thinsp;0.20 for severely burned areas, C\u0026thinsp;=\u0026thinsp;0.05 for moderately burned areas, and C\u0026thinsp;=\u0026thinsp;0.01 for areas that had burned at low severity. The support practice factor (P) was set for 1 since no land management practice was identified in the study site. The maximum theoretical SDR was set as 0.8 as recommended by Sharp et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the K and \u003cem\u003eIC\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e parameters are set to 2 and 0.5 respectively as explained by Vigiak et al. (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Carbon Storage and Sequestration\u003c/h2\u003e \u003cp\u003eCarbon sequestration and storage is an important ecosystem service provided by forests (Sohngen and Brown, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and carbon emissions are a notable ecosystem disservice arising from wildfires (Simmonds et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To evaluate the impacts of the Tubbs and Thomas fires on the carbon storage within our watersheds of interest, we first estimated changes in the biomass levels arising from wildfire-induced vegetation changes (see Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e). We then compared the aboveground carbon levels across both scenarios. We assume no land use changes before and after each fire. In practice, there could be changes in land, which has been demonstrated in other studies (Mockrin et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We convert above ground biomass (AGB) per acre to Carbon (or Carbon equivalent, as opposed to Carbon dioxide equivalent) per hectare using Eq.\u0026nbsp;1:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${C}_{hectare}=2.471* {AGB}_{acre}=1.2355*{AGB}_{acre} \\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe factor 2.471 is the conversion from Acres to Hectares, and 0.5 is the factor that converts dry weight biomass to carbon (as opposed to Carbon Dioxide equivalent; Li et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wirasatriya et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Once the conversion factor is applied to the biomass stores before and after the fire, we subtracted the total above ground carbon stored before the fire from the total above ground carbon stored after the fire and calculated the change in carbon pre- and post-fire.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Nutrient Delivery\u003c/h2\u003e \u003cp\u003eThe nutrient delivery module of InVEST was used to quantify the export and retention of nitrogen and phosphorus across the watersheds and to identify changes in nutrient export under land cover conditions before and after the fires in the two study areas. The module uses the simple mass balance concept to describe the long steady state flow of nutrients using empirical relationships (Sharp et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The model computes the nutrient export from each pixel based on nutrient sources on each LULC and the retention properties of the pixels belonging to the same flow path (Parn et al., 2012). The nutrient sources refer to nutrient applications across the LULC in the form of loadings and could be surface and subsurface sources (Hanshaw et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe DEM raster map utilized as input for this model was identical to the one for the SDR model, downloaded from the SRTM database and processed using ArcGIS Pro 3.0.3. The LULC maps utilized for the scenario experiment were the pre and post fire land cover maps. The nutrient runoff proxy for this study was the annual precipitation downloaded from Daymet. The runoff proxy is used to evaluate the spatial variability of runoff which has the capacity to transport nutrient downstream. The biophysical table for this model was filled up with data from extensive literature search. For the prefire nutrient delivery modeling, the nitrogen and phosphorus loadings for each unique land cover type were obtained from the nutrient analysis report prepared by Tetra Tech (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e), Fenn et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and nutrient database provided in the InVEST User guide (CITE). The retention efficiency eff, for each nutrient is the maximum nutrient retention expected from each LULC type. This ratio varies from 0 to 1, with high values (0.6\u0026ndash;0.8) assigned to natural vegetation, indicating that 60\u0026ndash;80% of nutrients are retained by these land cover (Sharp et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The critical flow length which describes the distance of travel required to achieve the nutrient retention coefficient was set to the resolution of the input LULC raster map. The proportion subsurface n and Borselli K parameter values were obtained from the user guide of the InVEST NDR module (Sharp et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For the postfire modeling, the nutrient loadings required as input of the NDR module were derived from published literature that focused on the contributions of wildfire on nutrient deposition. Based on the study of Koplitz et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Wright (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e1976\u003c/span\u003e), a 30% increase in Nitrogen loadings and about 38% increase in Phosphorus loadings were used to calibrate the post fire NDR biophysical table. Input data details and sources are provided in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Analysis of Wildfire Distributional Impacts to Communities\u003c/h2\u003e \u003cp\u003eAfter modeling the spatial distribution of regulating services before and after the fires, we additionally assessed the extent to which the Tubbs and Thomas fires led to changes in how ecosystem services and disservices are equitably or inequitably distributed across different communities, pre- and post-fire. We used Ordinary Least Squares (OLS) regression (Eq.\u0026nbsp;2) to investigate the extent to which several sociodemographic variables are associated with changes in water yield, soil loss, nitrogen loading, and phosphorus loading. Specifically, we overlayed the output rasters from each InVEST module on California Communities Environmental Health Screening Tool (CalEnviroScreen) US Census tracts (CEPAO, 2017). The spatial overlay allowed us to attribute InVest grid cells to US Census tracts. Each census tract had various InVest grid cells attributed to it. To estimate a tract-level quantity for each InVest variable, we took the mean of grid cells attributed to a particular census tract. As opposed to previous analyses described above, the data for the socio-demographic analysis were pooled together to ensure that the analysis has sufficient statistical power.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${y}_{i,h}={\\beta }_{0}+{\\beta }_{1}U+{\\beta }_{2}P+{\\beta }_{3}E+{\\beta }_{4}H+{\\beta }_{5}L+ϵ \\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{i,h}\\)\u003c/span\u003e\u003c/span\u003e is the InVEST variable before the fire (subscript \u003cem\u003eb\u003c/em\u003e) for a given US Census tract (subscript \u003cem\u003ei\u003c/em\u003e). The explanatory variables of the regression include the unemployment rate, \u003cem\u003eU\u003c/em\u003e, the poverty rate \u003cem\u003eP\u003c/em\u003e, the Education level \u003cem\u003eE\u003c/em\u003e, housing burden \u003cem\u003eH\u003c/em\u003e, and the linguistic isolation, \u003cem\u003eL\u003c/em\u003e. All explanatory variables are included as percentages. The definition of the unemployment rate, per OEHHA (2021), is: \u0026ldquo;Percent of the population over the age of 16 that is unemployed and eligible for the labor force\u0026rdquo; and poverty rate is defined as \u0026ldquo;Percent of population living below two times the federal poverty level\u0026rdquo;. The education level is defined as \u0026ldquo;Percent of population over 25 with less than a high school education\u0026rdquo; (OEHHA, 2021). Housing burden is defined as \u0026ldquo;Percent housing-burdened low-income households\u0026rdquo; (OEHHA, 2021). Finally, linguistic isolation is defined as \u0026ldquo;Percent limited English speaking households\u0026rdquo; (OEHHA, 2021). The error term is given by e and is assumed to be normally distributed and mean zero. The constant is \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, with the coefficients of the regression being the various \u003cem\u003eβ\u003c/em\u003es.\u003c/p\u003e \u003cp\u003eNext, we run a second OLS regression that examines the relationship of the sociodemographic variables with differences in the ecosystem services simulated by the InVEST before and after the fire, conditioning on the pre-fire levels of the ecosystem services simulated by InVEST.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${y}_{i,a}-{y}_{i,b}={\\beta }_{0}+{\\beta }_{1}U+{\\beta }_{2}P+{\\beta }_{3}E+{\\beta }_{4}H+{\\beta }_{5}L+{\\beta }_{6}{y}_{i,b}+ϵ \\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere the variables in the above regression are the same as in Eq.\u0026nbsp;1, with the exception that the dependent variable is the difference before and after the fire \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{i,a}-{y}_{i,b}\\)\u003c/span\u003e\u003c/span\u003e and the InVEST variable before the fire is included as an explanatory variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{i,b}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe effects of outliers are a larger concern for datasets with fewer observations. To limit the impact of outliers on the regression, we apply a hyperbolic arcsine transformation to all of the variables in equations 2 and 3. An added benefit of the hyperbolic arcsine transformation is that the interpretation of the coefficients in the regressions become approximations of percent changes (Bellemare and Wichman, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, the interpretation of any given coefficient from estimating equations (2) or (3) were that a 1 percent change in each sociodemographic variable on average results in a \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e percent change in either the pre-fire level of ecosystem service (Eq.\u0026nbsp;2) or the difference in pre- and post-fire ecosystem services, all in a given census tract (Eq.\u0026nbsp;3). For all statistical analyses we used the lfe software package (Gaure, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Annual Water Yield\u003c/h2\u003e\n \u003cp\u003eThe mean modeled annual water yield for the Pre-fire scenario in the Mark West subwatershed ranged between 168 mm and 945 mm, with a mean value of 699 mm. Post-fire land use map-based simulations show that the modeled mean annual water yield increased by 6% with a spatial range of 168 mm and 1032 mm, while the modeled spatial difference between the pre and postfire scenario ranges between 0 and 590 mm as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. A decrease in modeled actual evapotranspiration was also observed for the post-fire modeling scenario compared to the pre-fire scenario.\u003c/p\u003e\n \u003cp\u003eSimilarly, an increase in modeled annual water yield was observed in the Southern California watersheds after the fire event. The spatial variation in the modeled water yield upon comparing the pre- and post-fire scenario ranges between 0 and 347 mm as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. A difference of about 8.4\u0026nbsp;million m\u003csup\u003e3\u003c/sup\u003e was obtained in the modeled total annual water yield in the watershed, indicating a 42% increase in the modeled post fire annual water yield compared to the pre-fire scenario. The increase in modeled water yield could be attributed to the decrease in evapotranspiration and reduced interception due to changes in land cover.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Soil Loss and Sediment Delivery\u003c/h2\u003e\n \u003cp\u003eThe land cover alteration due to the fire affects the soil loss in both watersheds. The modeled total soil loss showed about 66% increase in the Mark West subwatershed. An increase was also obtained in the Southern California sub-watersheds, where the modeled post-fire soil loss increased to 578,779 tons from about 567,335 tons before the fire event as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The modeled spatial difference between the pre and post-fire soil loss in the Mark West subwatershed ranges between 0 and 486 tons/ha/yr, and in Southern California ranges from 0 to 3,116 tons/ha/yr as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSouthern California (SoCal) watersheds ecosystem service modeling\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrefire\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePostfire\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eSoCal Annual Water Yield\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Yield (mm3/yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,977,405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28,366,687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Consumption (mm3/yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80,687,266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101,133,453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoCal Soil loss and Sediment Delivery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil loss (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e567,335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e578,779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSediment Export (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23,118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24,308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSediment deposition (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e310,534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285,414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoCal Nutrient Delivery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrogen loads (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122,646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrogen export (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphorus loads (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35,513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45,910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphorus exports (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe same pattern was observed in the amount of modeled sediment exported from the land cover before and after the fire event. In the Mark West subwatershed, an increase of 49,961 tons of sediment was simulated in the post-fire SDR scenario, indicating a rise in sediment export induced by the fire event. The modeled spatial difference in sediment export for the Mark West subwatershed ranges between 0 and 94 tons/ha/yr, while that of Southern California sub-watershed extends from 0 to 139 tons/ha/yr as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Carbon Storage and Sequestration\u003c/h2\u003e\n \u003cp\u003eThe results for Total Above Ground Carbon and other categories are found in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Both fires resulted in large losses in total above ground carbon. According to our modeling, the Tubbs fire removed 199,318.57 tons of carbon from the Mark West subwatershed. The Thomas Fire\u0026rsquo;s impact was smaller and across a relatively larger land area. It removed 158,662.41 tons of carbon from the South Ventura, Harmon Canyon, and Arrundell subwatersheds. Together, the two fires resulted in an emission of 1,312,597 tons of CO\u003csub\u003e2\u003c/sub\u003e equivalent carbon. For context, annual US GHG emissions are about 6\u0026nbsp;billion tons of CO\u003csub\u003e2\u003c/sub\u003e equivalent carbon.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eChanges in total above ground carbon stores at the watershed level.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCarbon stores\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMark West\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSouthern California\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-30,160.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-52,545.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShrub\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-19,432.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-12,1875.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHerb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2,393.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-294.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-67,473.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2,062.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLitter Layer Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6,994.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-81,812.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGround\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-71,878.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5,181.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Above Ground\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-199,318.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-158,662.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Nutrient Delivery\u003c/h2\u003e\n \u003cp\u003eThe mean modeled Nitrogen export in the Mark West subwatershed was 0.012 Kg/ha/yr before the fire and 0.014 Kg/ha/yr after the fire, a substantial increase. The modeled spatial difference in Nitrogen exports in this same watershed ranges between 0.001 Kg/ha/yr and 0.04 Kg/ha/yr as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Nitrogen exports in the southern California sub-watersheds showed similar trends as the Mark West counterparts, with increased nitrogen exports after the fire event.\u003c/p\u003e\n \u003cp\u003eThe modeled mean difference in the nitrogen export before and after the fire in the watershed in Southern California watershed was 0.18 Kg/ha/yr and ranged between 0.001 and 2.34 Kg/ha/yr spatially as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. An 11% increase in modeled total Nitrogen export was observed in the Mark West subwatershed while about 14% increase was found in the Southern California watershed.\u003c/p\u003e\n \u003cp\u003ePhosphorus exports in the watersheds also increased in both watersheds, according to our models. The total phosphorus exports in the southern and northern California watersheds before the fires were 5,058 Kg and 1,248 Kg, respectively. These values increased by 20% and 16% in the Southern California and Mark West watersheds respectively as shown in table 2 and table 4. The spatial difference in phosphorus exports ranges between 0 and 0.916 Kg/ha/yr in Southern California sub-watersheds and 0 and 0.013 Kg/ha/yr in the Mark West subwatershed as shown in \u003cstrong\u003eFig. 6\u003c/strong\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMark West Sub-watershed ecosystem service modeling\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-fire\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePost-fire\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eMark West Annual Water Yield\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Yield (mm3/yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103,335,478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108,045,478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Consumption (mm3/yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81,040,398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71,521,375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMark West Soil loss and Sediment Delivery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil loss (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e541,020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e898,726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSediment Export (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44,332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94,293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSediment deposition (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e377,916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e612,338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMark West Nutrient Delivery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrogen loads (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28,635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31,705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrogen export (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphorus loads (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphorus exports(Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Distributional Impacts of Wildfire to Communities\u003c/h2\u003e\n \u003cp\u003eBecause the socio demographic analysis was performed on a pooled dataset containing observations in both watersheds, the presentation of the results differs from the previous results. Instead of presenting results for both the Mark West and Southern California watersheds, the results presented below are for both watersheds.\u003c/p\u003e\n \u003cp\u003eThe demographic variables included in both regressions did not have a substantial correlation with the distribution of the pre-fire InVEST outputs except in two distinct cases. First, census blocks that had higher levels of linguistic isolation were related to lower levels of annual pre-fire sediment loss, phosphorous loading, and nitrogen loading. Interestingly, linguistic isolation was not related with the pre-fire level of water runoff.\u003c/p\u003e\n \u003cp\u003eWater runoff, on the other hand, was negatively associated with unemployment, poverty, housing burden, and education, all of which are statistically significant at the 5% level (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; Column 1). Conversely, the amount of runoff was positively correlated with census blocks with higher education (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; Column 1). It is important to note that several of the results might be driven by topographical features inherent to the communities (e.g. slope). We then estimated Eq.\u0026nbsp;3, which included the pre-fire levels of each InVEST variable to obtain Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eMany of the differences between the InVEST variables before and after the fire were correlated with their pre-fire levels. For instance, the pre-fire levels of water yield were negatively correlated with changes in water yield following a fire ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta =-0.444\\)\u003c/span\u003e\u003c/span\u003e) such that the more pre-fire water yield there was, the lower the change due to wildfire was. Similarly, pre-fire levels of nitrogen and phosphorus loading were positively correlated with the change in nitrogen ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta =0.240\\)\u003c/span\u003e\u003c/span\u003e) and phosphorus ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta =0.306\\)\u003c/span\u003e\u003c/span\u003e) following a fire. Thus, the more pre-fire nitrogen and phosphorus there was, the higher the difference between pre- and post-fire levels of nitrogen. The pre-fire level of soil loss before the fire was not statistically significant in the regression.\u003c/p\u003e\n \u003cp\u003eThere were only three instances in which any of the socio-demographic variables were related to ecosystem service outcomes. Soil loss was positively correlated with housing burden. However, soil loss was negatively correlated with education. Further, housing burden was negatively correlated with nitrogen loading.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegression results for estimating the impacts of sociodemographic variables on the pre-fire levels of water yield, sediment loads, phosphorus, and nitrogen. All variables are transformed using the hyperbolic arcsine transformation, and the coefficients can be regarded as approximations of percent changes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSociodemographic variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable\u003c/em\u003e:\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-fire\u003c/p\u003e\n \u003cp\u003ewater yield\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-fire\u003c/p\u003e\n \u003cp\u003esoil loss\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-fire\u003c/p\u003e\n \u003cp\u003ephosphorus\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-fire\u003c/p\u003e\n \u003cp\u003enitrogen\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.141\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoverty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;1.038\u0026lowast;\u0026lowast;\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.380)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousing Burden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.370\u0026lowast;\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.183)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.370\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinguistic Isolation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.140\u0026lowast;\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.012\u0026lowast;\u0026lowast;\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.024\u0026lowast;\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.206\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.351)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.577)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.269)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegression results estimating the impacts of sociodemographic variables on the change in ecosystem services following a fire for water yield, sediment load, phosphorus, and nitrogen. All variables are transformed using the hyperbolic arcsine transformation, and the coefficients can be regarded as approximations of percent changes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable\u003c/em\u003e:\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference in water yield\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference in soil loss\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference in phosphorus\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference in nitrogen\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-fire water yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.444\u003csup\u003e\u0026lowast;\u003c/sup\u003e (0.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-fire sediment load\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003cp\u003e(0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-fire phosphorus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.306(0.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-fire nitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.240(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoverty rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.682)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.345)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousing burden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.284\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.013\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.319)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.219\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinguistic isolation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;2.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.517)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.444)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4.0. Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Annual Water Yield\u003c/h2\u003e \u003cp\u003eThe large increase in water yield occurring within the Southern California watersheds is consistent with the large and deadly debris flows occurring there following the Thomas Fire (Addison and Oomman, 2020). The differences in results between watersheds was likely driven by differences in landscape type. The Southern California watersheds are typically chaparral landscapes, which might have contributed to this difference, since chaparral landscapes might have particularly higher post-fire potential for increased runoff (Hubbert et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). One caveat is that while our analysis showed the change in annual water yield, the change in seasonal water yield might be different. For example, Adamowicz et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) showed that forests can act as a sponge and provide water during the dry season. Our results are consistent with that of different studies such as Saxe et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Underwood et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Blount et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) who also show an increase in annual water yield because of a fire event and climate change.\u003c/p\u003e \u003cp\u003eThe rise in water yield observed in the Southern California watersheds is also consistent with those observed by Kinoshita and Hogue (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in their assessment of the impact of the 2003 Old Fire event on two ephemeral basins in Southern California. The mean actual evapotranspiration in the watershed was also reduced from 844 mm in the pre-fire scenario to 700 mm in the post-fire scenario. Vegetations such as trees and shrubs are often characterized by taking up a significant amount of water through their roots, losing this water to the atmosphere through transpiration and interception of water through canopy covers Turner (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). However, in the event of forest fire, as in this study, vegetation destruction could lead to increased water yield in the watershed Basso et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Pereira et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, reduced evaporation from soils and other surfaces could increase streamflow into rivers and potentially increase water yield (Kinoshita and Hogue, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is a considerable amount of research that has examined how wildfire impacts water yield. Results from this body of work suggest that impacts can be mixed. For instance, forest fire events may affect soil permeability by reducing soil compaction, allowing water infiltration into the soil, and increasing yield as baseflow (Gonzalez-Romero et al. (2021). Other work shows that wildfire can increase the repellency of soils, resulting in more water yield (Hubbert et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Fire also affects soil hydraulics by depositing ash and sediments and developing water repellent CSIRO (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which could lead to increase in water yield Moody and Martin (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2001b\u003c/span\u003e). The long-lasting rise in water yield resulting from severe and widespread fires could modify the functioning of riparian ecosystems (Salemi et al., 2012). However, it may also offer a distinct possibility to supplement the regional water supply used by urban areas and agricultural communities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Soil Loss and Sediment Delivery\u003c/h2\u003e \u003cp\u003eWe also found an increase in the expected soil loss and erosion across both the Mark West subwatershed and the Southern California watersheds. While we provided the justification for the increase in soil loss and erosion based on the modeling approach, in practice, the increase in soil loss observed in the post-fire scenario could have also been due to the loss of organic matter and hydrophobicity associated with the burning of land cover. Fires can reduce the ability of the soil to hold moisture by burning organic matter, making the soil more erodible (Shakesby and Doerr, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Soil structure disruption associated with intense fire heat increases soil compaction and reduces infiltration capacity, making the soil vulnerable to erosion. Post-fire activities also lead to increased surface runoff and high flow with the potential of detaching and transporting significant soil particles. Ash deposition upon burning of land cover can lead to a hydrophobic layer on the soil, reducing water infiltration into the soil, which could result in high surface runoff and favor the removal of soil particles (Larsen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Alteration of channel morphology, such as increased channel roughness and formation of sediment deposits after a fire event associated with burnt land cover, could also enable higher sediment export (Moody and Martin, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2001b\u003c/span\u003e). Fires have a vast impact on channel morphology which influences sediment availability. Channels provide sediment through the upstream extension of head cuts, lateral bank erosion, and further destruction of banks (Moody and Martin, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eErosion rates and sediment yield have been widely documented to increase after fire events, often due to the direct and indirect effects of the burning (Moody and Martin (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2001a\u003c/span\u003e); Robichaud et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Biswas et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)). Other studies also report an increase in sediment yields as a result of wildfires. For example, East et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) showed that post-fire sediment yield in Whiskeytown National Recreation Area in Northern California increased after the Carr fire incident. In another study, using a physically based model Rulli and Rosso (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) find a substantial increase in erosion and sediment in nine basins in Southern California due to a series of wildfires. Finally, Coombs and Melack (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) found an increase in suspended sediment export from 82% burnt San Onofre watersheds characterized by chaparral vegetation in Southern California compared to similar unburnt watersheds.\u003c/p\u003e \u003cp\u003eThe rise in soil loss and sediment export could be related to vegetation loss induced by fire events Parise and Cannon (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This is caused by the loss of plant roots and above-ground biomass, which removes the protective cover that holds the soil and makes it susceptible to gradual soil loss. Forest and vegetation covers are important for binding soil particles together using a network of fibers in their roots which increases soil stability and prevents erosion Parise and Cannon (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The loss of above-ground biomass, such as leaves, stems, and branches, exposes the soil to the direct impact of rainfall and wind, which triggers the loss of soil particles. The loss of canopy cover in forested watersheds due to fire events reduces rainfall interception and increases the wind effect on the soil. Hanshaw et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) observed that rainfall amount measured by a rain gauge beneath a chaparral shrub canopy was reduced to about 42% than those observed in adjacent burnt areas. These actions in isolation or combination can increase the pressure on the soil surface and their erosive potential. Furthermore, vegetation loss due to wildfire could reduce the stabilization of slopes and increase soil erosion along the slopes due to landslide mass movement and increase the rate of sediment exportation (McEachran et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Carbon Storage and Sequestration\u003c/h2\u003e \u003cp\u003eThe differences in the carbon losses reflect the differences in the size of the watersheds and the differences in vegetative land cover across regions. For instance, while the Mark West lost 19,432.82 tons of carbon in the shrub category, the Southern California Watersheds lost 121,875.72. This represents approximately 0.3% of the 45 teragrams of total carbon storage in the Southern California National Forests reported by Underwood et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, the Southern California Watersheds lost far more carbon in the litter Layer Mass (LLM) category. The Mark West subwatershed lost substantially more carbon in the wood category (67,473.64 tons) than the southern California sub watersheds (2,062 tons). Interestingly, though the Mark West subwatershed lost 30,160 tons of carbon in the Canopy category, the three Southern California watersheds gained 52,545 tons in the year following the fire. This is likely due to the pattern of landscape changes that occur following a fire for fire-adapted chaparral ecosystems (Storey et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Nutrient Delivery\u003c/h2\u003e \u003cp\u003eOur analysis showed an increase in the expected post-fire phosphorus and nitrogen export. Increased phosphorus and nitrogen exports in the watersheds could be detrimental to downstream waterbodies by providing conditions that favors the rapid growth of algal and other invasive species. Comparing the nutrient exports with other studies in different study areas could be complex due to varying rainfall amounts triggering runoff generation, the variation in fire severity, post-fire activities, ecosystem characteristics that drive nutrient mobility, and variation in methods used for nutrient measurements (Lane et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); Smith et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2011\u003c/span\u003e);. However, studies such as Ferreira et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); Coombs and Melack (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); Goodridge et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also showed an increase in nutrient exports after a fire event. Coombs and Melack (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) specifically observed increased dissolved organic nitrogen (DON) and phosphorus exports in a burnt chaparral-dominated watershed in Southern California triggered by a high storm event and additions of Nitrogen in the form of ammonium and DON deposited as ash on the soil surface.\u003c/p\u003e \u003cp\u003eIncreased nitrogen and phosphorus exports in burnt watersheds can be related to the loss of vegetation cover (Rodr\u0026iacute;guez-Romero et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Vegetation cover plays a vital role in the uptake of nutrients from the soil. Their loss could lead to decreased nutrient retaining capacity and increased availability of these nutrients for export. Soil disturbance associated with burnt landscapes interferes with erosion intensity and soil compaction, which disrupts natural nutrient cycling and could lead to increased availability of Nitrogen and phosphorus to be exported Smith et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The enhanced export of Nitrogen and phosphorus from an ecosystem can also be considerably influenced by the ash deposition after a fire (Goforth et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Ash introduces a variety of organic and inorganic components, such as Nitrogen and phosphorus, when it falls on the soil\u0026rsquo;s surface. As a result, many mechanisms that improve nutrient export are implemented (Reneau et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Nutrients are easily freed from ashes, making them easier to distribute; however, InVEST does not consider this effect.\u003c/p\u003e \u003cp\u003eOn a regional basis, the increase in postfire nitrogen and phosphorus export observed in this study aligns with the observed trend in the western United States, often associated with elevated phosphorus and nitrogen levels during the first five years after a fire event (Rust et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These increases in nutrient exports have stringent implications on water quality and management strategies. Nitrogen and Phosphorus are often limiting nutrients for algal growth in freshwater and coastal systems. Their high occurrence is a recipe for eutrophication, potentially causing toxic algal blooms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Analysis of Distributional Impacts of Wildfire to Communities\u003c/h2\u003e \u003cp\u003eThe sociodemographic analysis found that differences in modeled water yield, soil loss, phosphorus, and nitrogen levels before and after wildfire were largely not correlated with sociodemographic variables. Rather, the pre-fire levels of these variables were correlated with sociodemographic variables. The implication of these results is that although ecosystem services are inequitably distributed across the landscape, wildfires neither exacerbated nor improved the distribution of ecosystem services, according to our modeling. It is important to note that this result might be specific to the collection of ecosystem services considered in this study, and to the study areas considered.\u003c/p\u003e \u003cp\u003eWhen estimating Eq.\u0026nbsp;(2), several relationships were found between ecosystem service provision and sociodemographic variables. For instance, a 1% increase in the poverty rate of a census tract was correlated with a 1.038% decline in water yield, such that the higher the poverty rate of a census tract, the less water was passing through the census tract. This observation was also true of housing burden, where a 1% increase in housing burden was correlated with a 0.37% decline in runoff, all else constant. Recent work has examined linkages to water and poverty levels. For instance, Alqatarneh and Al-Zboon (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) create a water poverty index for resource management in Jordan. In the United States, Deitz and Meehan (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) investigated hot spots for water inequality along racial and geographic lines, finding clear relationships between the quality of plumbing and race. The results of our study support findings in Deitz and Meehan (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in that we too find that, even in wildland systems, there is distributional inequality in water resources. This highlights the need for tools like those presented in Alqatarneh and Al-Zboon (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for helping to relieve inequalities.\u003c/p\u003e \u003cp\u003eWe also found that linguistic isolation is negatively correlated with soil loss, such that a 1% increase in linguistic isolation is correlated to a 0.14% decline in soil loss. There are similar relationships between linguistic isolation and phosphorus and nitrogen loading; however, the magnitudes of the changes are exceptionally small. Our results indicated that pre-fire levels of soil loss are lower in communities with fewer English speakers. Many studies look at soil pollution, rather than soil loss. For instance, Masri et al., (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) looks at soil lead distributions in the city of Santa Ana California, finding that soil lead was much higher in socioeconomically disadvantaged neighborhoods.\u003c/p\u003e \u003cp\u003eThough many studies have addressed how wildfires impact the provision of wildfires (Lee et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vukomonovic and Steelman, 2019; Pereira et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), to the best of our knowledge there is no other study that addresses the distributional impacts of wildfires on ecosystem services. However, the distributional equity of ecosystem service supply has been a well-studied issue. Much of the work on ecosystem services and environmental justice has been done in urban landscapes (e.g. Geneletti et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) with others expanding analysis to the WUI (e.g., Thomas et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; D\u0026rsquo;Evelyn t al., 2022). Our results showed that the inequality in the distribution of water, soil loss, nitrogen, and phosphorus loading are mainly driven by pre-existing inequalities that were present prior to wildfire. There are many other dimensions of inequality related to wildfire, including who is impacted by home loss and smoke (Thomas et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Future work might benefit from comparing inequality before and after fires and other large natural disasters to see how these events impact different kinds of inequality. Such work will be vital for identifying changes to policy needed to alleviate inequality.\u003c/p\u003e \u003cp\u003eA notable observation in these results is the lack of evidence for sociodemographic variables being correlated with either pre-fire ecosystem service levels or with differences in ecosystem service levels before and after fires. There is an ample literature which seeks to illustrate how sociodemographic groups might sort into areas of differing environmental quality (e.g. Mart\u0026iacute;n-L\u0026oacute;pez et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Faccioli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, one way to interpret our findings is that we do not find evidence to support this literature, at least in the landscapes which we included in this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"5.0. Conclusion","content":"\u003cp\u003eThis study assessed the effects of wildfires on annual water yield, carbon sequestration, soil loss, sediment exports, phosphorus delivery, and nitrogen delivery. We found that the magnitude of changes amongst each ecosystem service vary greatly between the watersheds studied. Increased annual water yield was observed for both Mark West and Southern California watersheds after wildfire event. We observed that after the wildfire event, about 11% and 14% nitrogen export increase was found in the Mark West and Southern California watersheds respectively. A 16% increase in phosphorus export was also observed in the Mark West sub-watershed compared to the 32% increase observed in the Southern California watersheds.\u003c/p\u003e \u003cp\u003eThe differences and similarities in how fire affected each region is important for post-fire management. Importantly, our modeling results were consistent with finding in Hubbert et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) in that chaparral landscapes have particularly high potential for runoff events following fire compared with non-chapparal landscapes; even leading to disservices in the form of floods. Consistent with Hubbert et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), even though the change in post fire runoff is large, changes in sedimentation, nitrogen, and phosphorus levels of the water are not high.\u003c/p\u003e \u003cp\u003eThere are several important limitations and avenues for future research. A relevant limitation of the annual water yield model for this study is the lack of spatial distribution of land cover such that complex land use changes are not adequately characterized. This limitation means that complex changes in land use may not be comprehensively accounted for in model predictions (Sharp et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e. The InVEST model was selected due to its flexibility, interpretability, and reproducibility; however, other modeling frameworks might provide more accurate results for focused studies, such as Soil and Water Assessment Tool (SWAT). Further, there are a wide variety of other ecosystem service bundles that could be considered. We limited our analysis to carbon, water quantity, and water quality due to the salience and importance of these services in the California context; however, air pollution, recreation, and ecosystem disservices are also important to consider by future research. Past studies on the topic have also considered the economic values of these ecosystem services (e.g. Underwood et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Future work can combine flexible benefit transfer methodologies with modeling output to achieve estimates of economic damage resulting from the fire. However, this might be challenging, especially for water resources, since the per-unit values of water resources might be dependent on the volume.\u003c/p\u003e \u003cp\u003eAlthough our study did not account for the ability of different communities to adapt to changes, nor did the distributional impacts to ecosystem services did not change, procedural justice and economic inequalities might still result in an inequal distribution of benefits and even disservices to certain communities. Future work can benefit from incorporating the community level adaptive capacity into post-wildfire recovery. The relationship between natural disasters and the provision of ecosystem services is a long-studied topic that still commands attention from various disciplines. As wildfires continue to affect human communities more, understanding their impacts will become more necessary. This study provides further understanding of the importance of landscape and sociodemographic characteristics in determining the distribution impacts to ecosystem services following wildfire.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI.B: Conceptualization, Data Curation, Formal Analysis, Original Draft, Writing-Review and EditingM.S and M.R: Conceptualization, Formal Analysis, Writing-Review and EditingD.S: Conceptualization, Writing-Review and Editing, Project ManagementS.D and FE: Formal Analysis, Writing-Review and Editing\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge the US Department of Agriculture-Forest Service under award number 21-JV-11272131-037.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamowicz, W., Calderon-Etter, L., Entem, A., Fenichel, E. P., Hall, J. S., Lloyd-Smith, P., Ogden, F. L., Regina, J. 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International Journal of Disaster Risk Reduction, 98, 104065.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emvm","sideBox":"Learn more about [Environmental Management](http://link.springer.com/journal/267)","snPcode":"267","submissionUrl":"https://submission.nature.com/new-submission/267/3","title":"Environmental Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Fire impacts, Ecosystem Services, Soil Carbon, InVEST, Tubbs Fire, Thomas Fire","lastPublishedDoi":"10.21203/rs.3.rs-4189499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4189499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEcosystem services are important for human well-being and maintaining environmental quality objectives. The growing concern over extreme wildfire events in various watersheds necessitates understanding their impacts particularly on regulating ecosystems services. In this study, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to examine how two wildfires that occurred in California, USA in 2017 impacted water provisioning, soil loss and sediment delivery, carbon sequestration services, and nutrient delivery in the waterways. We also related the distributional impacts of wildfire to ecosystem service supply based on various sociodemographic factors across the affected communities to assess their vulnerabilities. We find that a year following the fires, the amount of biomass in forestland, woodland, and chaparral declined, as expected, in both studied watersheds, while the amount of grassland increased. This change in vegetation resulted in the loss of about 200,000 tons of carbon from the Mark West subwatershed and about 160,000 tons of carbon from the Southern California watersheds. Furthermore, the fires increased the expected mean annual water yield significantly for both watersheds by 5% and 42%, respectively. Our analysis shows an increase in the expected post-fire phosphorus and nitrogen export. Using regression analyses to determine the effect of wildfire on the distributional impacts to ecosystem services across communities in the watersheds, we did find evidence of differences between communities with respect to the pre-fire distribution of ecosystem services. However, we did not find that post-fire condition either exacerbated or alleviated these distributional impacts and inequities.\u003c/p\u003e","manuscriptTitle":"Wildfire effects on ecosystem services in two disparate California watersheds: A Case Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-03 17:14:34","doi":"10.21203/rs.3.rs-4189499/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-29T08:26:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-23T23:59:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165128608262756908810889941490149975247","date":"2024-06-25T05:57:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"f9b10201-b403-4726-8ad5-49ae41b547b5","date":"2024-04-05T13:38:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-03T00:39:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-30T22:29:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-30T10:51:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Management","date":"2024-03-29T19:19:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emvm","sideBox":"Learn more about [Environmental Management](http://link.springer.com/journal/267)","snPcode":"267","submissionUrl":"https://submission.nature.com/new-submission/267/3","title":"Environmental Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1c51959a-f3a1-46c8-8d32-957a81178fc0","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:12:04+00:00","versionOfRecord":{"articleIdentity":"rs-4189499","link":"https://doi.org/10.1007/s00267-025-02185-3","journal":{"identity":"environmental-management","isVorOnly":false,"title":"Environmental Management"},"publishedOn":"2025-05-23 15:57:09","publishedOnDateReadable":"May 23rd, 2025"},"versionCreatedAt":"2024-04-03 17:14:34","video":"","vorDoi":"10.1007/s00267-025-02185-3","vorDoiUrl":"https://doi.org/10.1007/s00267-025-02185-3","workflowStages":[]},"version":"v1","identity":"rs-4189499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4189499","identity":"rs-4189499","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}