Southern forests contribute significantly to the carbon sink for the atmospheric carbon dioxide (CO2) associated with the anthropogenic activities in the United States. Natural disasters like hurricanes are constantly threatening these forests. Hurricane winds can have a destructive impact on natural vegetation and can adversely impact net primary productivity (NPP). Hurricane Katrina (23–30 August 2005), one of the most destructive natural disasters in history, has affected the ecological balance of the Gulf Coast. This study analyzed the impacts of different categories of sustained winds of Hurricane Katrina on NPP in Mississippi. The study used the Carnegie–Ames–Stanford Approach (CASA) model to estimate NPP by using remote sensing data. The results indicated that NPP decreased by 14% in the areas hard hit by category 3 winds and by 1% in the areas hit by category 2 winds. However, there was an overall increase in NPP, from 2005 to 2006 by 0.60 Tg of carbon, in Mississippi. The authors found that Pearl River, Stone, Hancock, Jackson, and Harrison counties in Mississippi faced significant depletion of NPP because of Hurricane Katrina.
The United States contributes about 0.30–0.58 Pg of carbon to the global carbon sink each year (Chambers et al. 2007). Southern forests contribute significantly to the carbon sink for the atmospheric carbon dioxide (CO2) associated with the anthropogenic activities in the United States (Chapman et al. 2008). Damage caused by natural disasters, such as hurricanes, to the structural components of forest ecosystems has wide-ranging effects on fauna and flora (Cooke et al. 2007) including an impact on the forest’s ability to sequester carbon. In the long run, however, forests disturbed by hurricanes could face change in vegetation composition and productivity resulting from storm-generated nutrients. Recovery of forest productivity is a long process: McNulty (McNulty 2002) observed that the conifer forests in South Carolina took 14 years to regain pre–Hurricane Hugo productivity.
Hurricane Katrina (23–30 August 2005) was the costliest and one of the five most deadly hurricanes to strike the United States. Katrina took about 1500 lives (Knabb et al. 2005). Along the Mississippi coast, the Katrina-induced storm surge was about 7–8 m high and about 32 km wide. The storm surge penetrated around 10 km inland in many parts of coastal Mississippi and around 20 km inland along bays and rivers (Fritz et al. 2007). The devastating winds of Katrina also destroyed forests and habitats in Mississippi, Louisiana, and Alabama (Sheikh 2006). As a result, many trees were defoliated, broken, and uprooted by the winds, creating massive amounts of dead biomass. The hurricane damaged around 521 million trees in Mississippi alone (Oswalt et al. 2008). Damage was more prominent in the areas affected by the category 3 winds (based on the Saffir–Simpson hurricane scale; Oswalt et al. 2008).
There are several studies (Masozera et al. 2007; Farris et al. 2007) on Katrina’s impacts on socioeconomic conditions, water quality, and potential fire hazards. However, there is a need for studies on the impact of Katrina on vegetation conditions and the primary productivity of the southern forests. Terrestrial net primary productivity (NPP), the fundamental ecological variable of terrestrial ecosystems, helps remove carbon from the atmosphere (Field et al. 1995). NPP is directly related to the carbon dynamics in the processes of photosynthesis and autotrophic respiration. It acts as the major driver of the seasonal fluctuations in atmospheric CO2 concentration (Ciais et al. 1995; Churkina and Running 1998). An understanding of the impact of sustained hurricane winds on ecosystems is essential for disaster preparedness programs and mitigation of environmental change. These studies can also help shape policies on global environmental change, especially on global carbon trade. In this study, we investigated the impacts of different category wind speeds of Hurricane Katrina on NPP in Mississippi using the Carnegie–Ames–Stanford Approach (CASA) model (Potter et al. 1993; Potter et al. 2006; Khanal 2009).
Forests in the southeastern United States play an important role in national greenhouse gas emissions. Southern forests account for approximately 29% of above-ground forest carbon stock in the conterminous United States (Mickler et al. 2004). There are several biodiversity hotspots in the south, such as the southern Appalachians, the panhandle of Florida and Alabama, and the Everglades (Linder 2004). Although the southeast region remains relatively heavily forested, much of the region’s current forest exists as tree plantations. Changes in the frequency of natural and human sources of disturbance have severe implications for many southeastern ecosystems. The southeastern states, ecologically important because of their high primary productivity, on the one hand, are undergoing rapid human-induced changes in land use and land cover (Milesi et al. 2003); on the other hand, they are also experiencing natural disturbances such as hurricanes (Oswalt et al. 2008; McNulty 2002).
Hurricane Katrina was one of the strongest storms to affect the southern coastal areas of the United States in the past 100 years. With sustained winds of 125 mph and a minimum central pressure record of 920 mb during landfall, Katrina caused widespread devastation of vegetation along the central Gulf Coast states (Graumann et al. 2005). Mississippi was one of the states that experienced a devastating impact on its economy and ecosystem because of Katrina. Hancock, Harrison, Jackson, and Stone counties were some of the Mississippi counties severely affected by Katrina. These counties contain about 9% of the forests in Mississippi (Oswalt et al. 2008).
Hurricanes alter landscape-scale patterns of forest structure and their composition, habitat availability, distribution, and susceptibility to subsequent disturbances (Oswalt et al. 2008). Apart from the studies (Farris et al. 2007; Masozera et al. 2007) that have analyzed the impact of Hurricane Katrina on environmental, social, and biophysical aspects of the affected areas, several studies have tried to analyze the impact of Katrina on forest vegetation. Rodgers et al. (Rodgers et al. 2009) investigated the vegetation changes in the Weeks Bay Reserve after Hurricane Katrina using normalized difference vegetation index (NDVI) data and found that the NDVI values were suppressed after Katrina because of the increased salinity. Cooke et al. (Cooke et al. 2007) assessed the pre- and post-Katrina fuel conditions in southern Mississippi and found that the areas with a very low fire hazard decreased from 19% to 3%, whereas the areas with a very high hazard increased from 3% to 13%. McNulty (McNulty 2002) studied the impacts of Fran, Hugo, Camille, and 1938 hurricane on U.S. forest carbon sequestration to find that a single storm can convert the equivalent of 10% of the total annual U.S. carbon sequestration to dead and downed biomass. However, Chambers et al. (Chambers et al. 2007) estimated that an amount equivalent to 50%–140% of the net annual U.S. carbon sink in forest trees was lost because of Katrina. Given that forests require approximately 15–20 years to recover from a storm (Conner 1998), a large amount of accumulated forest carbon is lost either directly or indirectly because of hurricanes (McNulty 2002). Between 1851 and 2000, an average of 97 million trees are affected each year in the United States because of hurricanes, with a 53-Tg annual biomass loss and an averaged carbon release of 25 Tg yr−1 (Zeng et al. 2009). The biomass loss because of Hurricane Katrina was up to 77.6 Mg ha−1 in the most severely damaged forests (Chapman et al. 2008). Thus, an extreme event such as Hurricane Katrina could radically reduce carbon sequestration in the areas affected for several decades (Feagin 2009). However, the full impact of the storm on carbon storage depends on how the forest productivity is affected. Therefore, an estimation of NPP after Hurricane Katrina will be an important contribution to the global climate change studies.
3. NPP estimation
NPP is influenced by various physiological and biophysical processes, some of which are very difficult to quantify and are thus rarely measured (Clark et al. 2001). Above-ground terrestrial NPP is one of the most commonly modeled ecological parameters. Studies have estimated NPP at global and regional scales using various models ranging from a simple correlation model (Lieth 1975) to complex ecophysiological models (Potter et al. 1993; Running and Nemani 1988). NPP estimation on regional and global scales involves the use of remotely sensed data and weather data. There are various models proposed to estimate NPP. The “Miami Model” (Lieth 1975) was the first global-scale empirical model of terrestrial NPP and is a simple empirical model. Other models include C-flux models such as the Terrestrial Ecosystem Model (TEM; Raich et al. 1991) and the Biogeochemical Model (BGC; Running and Hunt 1993). The BGC model was originally developed for conifer forests and is particularly sensitive to leaf area index (LAI) derived from satellite spectral measurements; it is a widely applied model to simulate NPP over large areas. CASA is a process-based model that integrates biophysical parameters such as NDVI and photosynthetically active radiation (PAR) and climatic factors such as temperature, precipitation, and evapotranspiration to estimate NPP (Potter et al. 1993; Potter et al. 2006; Field et al. 1995; Khanal 2009). Among all these models, the CASA and BGC models are most commonly used in the estimation of NPP on regional and global scales. Unlike the BGC, the CASA model has the ability to estimate NPP directly with the use of freely available satellite and biophysical data. These two models make use of satellite remote sensing data that provide nearly continuous observation over large areas required in the estimation of NPP on regional and global scales. The U.S. Geological Survey (USGS) Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) are the two remote sensors that have been commonly used to estimate the NDVI required in the estimation of NPP (Hicke et al. 2002; Rasmussen 1998). In this study, we used the CASA model and AVHRR satellite images to estimate NPP before and after Katrina.
In this study, we investigated the impact of different category wind speeds of Hurricane Katrina on NPP in Mississippi with the CASA model. PAR, fraction of photosynthetically active radiation (FPAR), light-use efficiency (ɛ), temperature (T), and water (W) scalars were used as the driving variables in the model (Potter et al. 1993). NPP (g C m−2) was calculated for each month in the years 2005 and 2006. The minimum mappable unit used in the study is 1 km.
In Equation (1), PAR(x, t) is the portion of the sunlight spectrum from 400 to 700 nm that is useful to terrestrial plants during the photosynthesis process. Incident PAR is required to model photosynthesis of vegetation. Unfortunately, only a few stations in the United States measure PAR. Monthly PAR values available at 0.5° resolution were collected from the Global Energy and Water Cycle Experiment (GEWEX) Continental Scale International Project (GCIP) database (available online at http://www.meto.umd.edu/~srb/gcip/) and were interpolated to the study area using the inverse distance-weighted method. The GEWEX project validates surface radiative fluxes with ground observation data. Among the six observation stations, one is in Goodwin Creek, Mississippi. The satellite-estimated PAR values are highly correlated to the ground-observed data (r values ranged from 0.93 to 0.98) in the Mississippi station.
The second variable, FPAR (x, t), is the measure of the amount of incident visible light absorbed by plant tissues. As absorption of light drives the photosynthesis process, FPAR is directly related to photosynthesis and thus to the rate of carbon fixation (Field et al. 1995; Running and Nemani 1988; Running et al. 2004; Sellers 1987). FPAR has a linear relationship with the simple ratio (SR; Sellers 1987) as expressed in the Equation (2),
NDVI is the indicator of relative abundance and condition of green vegetation (Jensen 1996; Running et al. 2004). NDVI was calculated using the red and near-infrared (NIR) wavelengths of the electromagnetic spectrum as in Equation (4),
USGS provides 14-day NDVI composites of 1-km resolution AVHRR satellite data. Monthly NDVI values were estimated using AVHRR data. In Equation (3), SRmin represents the simple ratio for unvegetated land areas and has been set to 1.08 (Sellers 1987). The value of SRmax was derived from an average of maximum SR value from the previous 11 years of data for each pixel.
The third variable, T1, in Equation (1) is a stress term at a very low or very high temperature, whereas T2 is a stress term when temperature is below or above the optimum temperature. Temperature scalars (T1 and T2) were computed using Equations (5) and (6), respectively,
In Equations (5) and (6), T is the mean monthly temperature and Topt is the mean temperature during the month of maximum NDVI (Potter et al. 1993). Monthly-mean temperature data were computed based on the monthly minimum and maximum temperature data collected from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) group database (available online at http://www.prism.oregonstate.edu/).
Water scalar W in Equation (1) measures the effect of precipitation and soil moisture on productivity. Monthly precipitation data were collected from the PRISM group. The value of W was calculated as the ratio of estimated evapotranspiration (EET) to potential evapotranspiration (PET),
where PET is a function of temperature and latitude (Thornthwaite 1948) and EET is the function of precipitation p, PET, and soil water-holding capacity. The soil water-holding capacity data were collected from the National Oceanic and Atmospheric Administration (NOAA; available online at http://www.cdc.noaa.gov/data/gridded/data.pdsi.html). PET was calculated using Equation (8),
where T is the mean surface air temperature (°C) in months i, I is the heat index, and the exponent a is the function of the heat index; I and a are estimated using Equations (9) and (10), respectively,
The computed PET was adjusted for day length as seen in Equation (11),
where d is the length of the month in days and h is the duration of daylight in hours on the 15th day of the month. EET is calculated as the function of p, PET, and soil water-holding capacity (Mehta 2006).
When p > PET, EET = PET, else EET = change in soil water + p.
In this model, we used the maximum light-use efficiency ɛmax of 0.389 PAR MJ−1 (Potter et al. 1993; Potter et al. 2006; Field et al. 1995). This uniform ɛmax value was initially derived from calibration of predicted annual NPP to previous field estimates from different parts of the world and in different land-cover classes and biomes (Potter et al. 1993).
All the parameters in the model were converted to 1-km resolution grids. Using ArcGIS and Erdas imagine software, the NPP for each month in 2005 and 2006 was estimated. Modeled NPP included the entire United States. The NPP estimations were evaluated across various BigFoot sites. BigFoot site NPP data are available in the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC; available online at http://daac.ornl.gov/cgi-bin/dataset_lister.pl?p=13). BigFoot NPP surfaces were developed to validate the satellite-based NPP estimation (Khanal 2009). Each BigFoot site covers up to a 5 km × 5 km area surrounding the CO2 flux towers located at each of the nine sites. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needle-leaf forest; temperate cropland, grassland, and deciduous broadleaf forest; desert grassland; and shrub land. At each site, carbon content and NPP are quantified that can be used to validate the satellite-based NPP calculations (Cohen et al. 2009).
After the accuracy assessment of the estimation, only the Mississippi area was extracted for further analysis. Hurricane Katrina’s track and wind speed category (Simpson 1974) data were then overlaid on NPP grids. Hurricane wind speed is classified into five categories: category 1 (74–95 mph), category 2 (96–110 mph), category 3 (111–130 mph), category 4 (131–155 mph), and category 5 (>155 mph; Simpson 1974). Average NPP values were estimated for the years 2005 and 2006 in areas that were affected by these five categories of wind speed. Zonal operation tools in ArcGIS software were used to extract mean NPP (g C m−2 yr−1) in each wind speed category of Hurricane Katrina.
Mean NPP values for all 83 counties in Mississippi were also extracted using zonal statistics. This provided the basis for comparing NPP in different counties and the areas affected by different wind speeds of Hurricane Katrina. Similarly, the impact of Katrina on different land covers of Mississippi was analyzed based on the average NPP values extracted from different land-cover classes. A vertical profile of NPP values in percentage from 2005 to 2006 was drawn to show the changes in NPP values along the hurricane track. A zonal operation was conducted on land-cover classes in Mississippi to analyze the impact of Hurricane Katrina winds on NPP production in each land-cover class.
Modeled NPP values had a significantly very high correlation (r = 0.88) with the BigFoot site’s NPP values. The results showed that the total NPP in Mississippi increased between 2005 and 2006 by about 0.60 Tg of carbon (Table 1). However, areas affected by category 3 winds (111–130 mph) during Katrina witnessed about a 15% decrease in NPP. NPP also decreased in areas affected by category 2 (96–110 mph) winds by about 1%, whereas NPP in the areas affected by category 1 (74–95 mph) winds and not affected by the hurricane showed an increase in NPP by about 1% each.
An analysis-of-variance (ANOVA) test indicated the significant relationship between mean NPP reduction and wind categories (F = 3.529, p = 0.02, and df = 4). A post hoc test [least significant method (LSD)] indicated that mean NPP values in the category 3 wind area were significantly different from mean NPP values in category 0 (p = 0.041), category 1 (p = 0.03), and category 2 (p = 0.05) wind areas. However, differences in NPP values between category 0, 1, and 2 wind areas were found to be insignificant.
Different categories of wind affected NPP differently in different land-cover classes (Table 2). Almost all land-cover classes in areas affected by category 3 winds had lower NPP in 2006 compared to 2005. Urban areas with medium density trees had 29% lower NPP in 2006. Because of the huge destruction caused by the category 3 winds, herbaceous wet lands, croplands, evergreen forests, woody wetlands, grasslands, and scrub showed more than a 10% loss in annual NPP in 2006. However, NPP values increased in the year 2006 in areas not affected by the hurricane winds.
Figure 1 shows the spatial pattern of change in NPP values in Mississippi after the devastation of Katrina. There was a decrease in NPP values in 2006 in the northwestern region of the state, which was not affected by the hurricane. NPP values were found to be affected by hurricane winds in the eastern parts of the state along the hurricane’s path. Figure 1 shows the decreased values of mean NPP in the hardest-hit counties of Mississippi. Mean NPP g C m−1 yr−1 decreased in several counties such as Pearl River (by 16 g C m−1 yr−1), Stone (by 28 g C m−1 yr−1), Hancock (by 90 g C m−1 yr−1), Jackson (by 9.26 g C m−1 yr−1), and Harrison (by 68 g C m−1 yr−1) between the years 2005 and 2006. Overall, NPP decreased in 37 of 83 counties in Mississippi.
A vertical profile of percentage change in NPP values along the hurricane track (Figure 2) clearly shows that NPP values were lower in the areas affected by category 3 winds along the hurricane track. NPP values showed a decreasing trend in the areas affected by category 2 winds along the hurricane’s path.
Hurricanes cause structural damage to numerous components of ecosystems. Damage to vegetation caused by hurricanes could affect a forest’s ability to sequester carbon. Southern forests are areas of significant carbon sinks and are frequently attacked by the devastating winds of hurricanes. In this study, we estimated NPP values in Mississippi pre– and post–Hurricane Katrina. We used the CASA model, which incorporates a combination of remote sensing data on vegetation and biophysical parameters such as radiation, temperature, light use efficiency, precipitation, and soil water holding capacity. The study found that the areas affected by category 3 winds had a sharp decrease in NPP compared to other regions. NPP decreased by 14% in the areas hit hard by category 3 winds and by 1% in the areas hit by category 2 winds, respectively. However, there was an overall increase in NPP from 2005 to 2006 by 0.60 Tg of carbon in Mississippi. Almost all land-cover types showed lower NPP after the hurricane. Urban areas with medium density trees had 29% lower NPP in 2006. We found that Pearl River, Stone, Hancock, Jackson, and Harrison counties of Mississippi faced significant depletion of NPP because of Hurricane Katrina.
In the long run, forests disturbed by hurricanes could increase their productivity because of increased storm-induced nutrients (McNulty 2002). This study showed that category 3 wind speeds could reduce NPP values and thus lower the carbon sequestration of the forested areas.
Immediately after any major disturbance such as a hurricane, a forest stand commonly acts as a source of carbon to the atmosphere until respiration from decomposers becomes less than photosynthetic uptake from regrowing vegetation (Goward et al. 2008). The age at which a forest becomes a net carbon sink varies according to forest type, site productivity, and a host of other factors (Goward et al. 2008). Because NPP is one of the integral indicators of the carbon cycle, long-term monitoring of NPP values in different adverse events like hurricanes is important to assess the change in vegetation composition and thus in carbon sequestration.
We would like to thank the reviewers for critical comments and suggestions. Thanks to Calvin Lim for helping us at various stages of the study.
* Corresponding author address: Shrinidhi Ambinakudige, Department of Geosciences, 355 Lee Blvd., Mississippi State University, Mississippi State, MS 39762. firstname.lastname@example.org