Impact of 1985 Hurricanes on Isles Dernieres, Louisiana:Temporal and Spatial Analysis of Coastal Geomorphic Changes, Karolien Debusshere, Karen Westphal, Shea Penland, and Randolph McBride
Petrology and Hydrocarbon Potential of Carbonate Beds WithinFerry Lake Anhydrite, Caddo-Pine Island Field, Caddo Parish, Louisiana, ColinE. Kimball, Leonard M. Young, E. G. Anderson, and Austin A. Sartin
Erosion and Deterioration of Isles Dernieres Barrier IslandArc, Louisiana: 1842-1988, Randolph A. McBride, Karen Westphal, Shea Penland,Bruce Jaffe, Abby Sallenger, and S. Jeffress Williams
The 24 rainfall events potentially caused erosion during the observation period ( Table 2). The soil loss prediction for bare land conditions by USLE was 0.57 kg per plot on day 7 (the first rainfall event after transplanting) and 2.82 kg (after the first weeding).
The estimates of K factors are based primarily on percentage of silt, sand, and organic matter and on soil structure and permeability ( USDA Natural Resources Conservation Service in 2009). Many calculation methods have been proposed for K factor such as Revised Universal Soil Loss Equation (RUSLE2), Erosion Productivity Impact Calculator (EPIC), and Geometric Mean Diameter based (Dg) model; however, the versatility of USLE for some soils are higher than that of the new method ( Wang et al., 2016). Thus, there is a large room to find suitable land for agriculture from the area of specified unsuitable for agriculture. Not erosion itself, but a related story, the use of USLE to predict sediment yields is not advisable despite their present widespread application ( Boomer et al., 2008).
Low erosion land for agriculture can be found by measuring erosion locally. The results obtained from a limited field, still, are important for Palau's agricultural development, and the results of this test can be regarded as reference.
A) The flow of discussion has been improved as follows. 1) Summarizing the results of the experiment, 2) Considering that the experiment meets the conditions for applying the USLE prediction formula, 3) Pointing out the gap between the predicted value and the measured value, and that the gap is derived from K-factor, 4) Showing that various improvement proposals have been shown for K factor, but there are cases where the original is more accurate depending on the soil, and the definitive calculation method has not been determined. 5 ) As a result of the above, there is a good possibility of finding suitable land in an unsuitable land for agriculture in Palau, and the results of this test can be regarded as an example.
In the site description, add information about the landscape setting, outside of the study site boundaries. A study site topographic map would be helpful. Information about the distribution of croplands on the study island, as well as a brief description of how the island compares to others in the region, would be helpful to thinking about the value of information beyond the study site.
Out of several land degradation methods, soil erosion is one of the major causes that deteriorate land quality. Soil erosion is a natural process that results in the removal and transportation of topsoil into downstream areas [4,5,6,7]. It occurs due to various causative factors such as rain, wind, and gravity whereas this process is gradually induced by human activities . As explained by the Food and Agriculture Organization of the UN , the major steps of the erosion process are soil loosening, soil transportation, and soil deposition. Therefore, as the ultimate result of this process, topsoil along with its contaminants viz. nutrients, agrochemicals, and fertilizers are transported and accumulated in downstream surface and groundwater sources [10,11]. Apart from the natural factors, human activities such as deforestation, inappropriate farming practices, and inappropriate land management practices have significantly induced the rate of soil erosion in the twentieth century [12,13,14]. Agricultural activities that include plantations are probably one of the most prominent anthropogenic activities that accelerate the rate of soil erosion since the topsoil is disturbed when preparing lands for cultivation . Therefore, it has been revealed that this human-induced soil erosion might negatively affect soil fertility in agricultural lands , drinking water quality , and natural ecosystems . Ultimately this causes long-term crop productivity losses , economic losses, food scarcity, and water security losses . Around 85% of global land degradation is occurred due to soil erosion which reduces crop yield by 17% .
The main objective of this study was to develop a soil erosion hazard zone map for Sri Lankan plantations that differ in terms of crops, topography, elevation, climatic conditions, and management practices, using a novel modelling approach. In order to achieve this objective, InVEST SDR model was used in this study and the findings of the current study could be used by the government and other agencies when developing land management policies in the plantation industry in Sri Lanka.
As shown in Figure 2, the major input data of the model are digital elevation model (DEM), rainfall erosivity factor (R) map, soil erodibility factor (K) map, crop management factor (C) data, and support practice factor (P) data of each land use land cover (LULC) type. The InVEST SDR model works at a finer spatial resolution of the DEM raster, and ultimately the model estimates the amount of annual soil loss that occurs from the pixel by integrating input data according to the USLE . The total soil loss map and the soil erosion potential (RKLS) map are the major outputs of the model. The soil erosion hazard zonation map can be generated using the model outputs .
Based on the generated LULC map, the plantation field consists of 22 LULC types including crop types of namely rubber, coconut tea, cinnamon, pepper, and paddy (refer to Figure 5). The percentage area of each crop type was calculated using the Tabulate Area Tool of the ArcGIS software. According to the calculations, rubber occupied the largest area covering 64.6% of the total land area. It was followed by natural forests comprising 9.8% and scrublands comprising 5.6%. The area also consisted of other crops viz. coconut (4.7%), tea (1.4%), paddy (0.2%), cinnamon (0.1%), and pepper (
The biophysical table is one of the most significant inputs of the model which is shown in Table 1. According to the formulated biophysical table, the C factor values for each LULC type ranged from 0.001 to 0.2 (refer to Table 1). Generally, C factor values ranged from 0 to 1.5 whereas finely covered land areas with proper crop systems are assigned nearly 0 values and higher values are assigned for highly vulnerable LULC types for soil erosion . However, there are two derivation methods of the C factor available as a derivation of C factor using Normalized difference vegetation index (NDVI) techniques and derivation of C factor values based on previous studies for similar LULC types in similar geographical regions . It was reported that 93% of the soil erosion modeling studies that were conducted in Sri Lanka have used previous literature to determine the C factor since the NDVI method is time-consuming, labour intensive, and requires advanced technology viz. satellite imagery, aerial photography, and image processing . The P factor value for the selected plantation varied from 0.001 and 0.35 (refer to Table 1). According to Bagherzadeh , the P factor ranges from 0 to 1 whereas near 0 values are assigned to LULC types with good land management practices and nearly 1 assigned to LULC types with poor land management practices which allow the soil to erode easily. P factor can also be utilized as a direct recognition of how soil conservation practices are effective in land management practices.
Similar to the C factor, generally, the P factor also can be obtained from the previously published literature related to the interested area or region where the study has been conducted. Moreover, 86% of Sri Lanka soil erosion modeling studies have used previously published data for obtaining P factor values for each land use and land cover class . Other 14% of studies have omitted using the P factor when developing soil erosion models. Furthermore, in order to develop soil erosion models more precisely and accurately, the employment of geographical-specific data is vital . Since the development of such methods is labor-intensive and time-consuming, already published data were used in the previous studies. In the current study, we also have used previously published data although area-specific data are recommended by the authors.
The RKLS is one of the outputs obtained after the simulations (Figure 6). Soil erosion potential is described as soil loss values in bare soil . Therefore, RKLS was calculated based on only R, K, and LS factors while excluding C and P factors which describe the effect of LULC types and land management practices.
The USLE map (Figure 8) was classified into five soil erosion hazard levels (refer to Table 2) and the area percentages of each hazard level were calculated using Zonal Geometry Tool in ArcGIS 10.4 software [18,37]. The results revealed that 49.5% of the plantation belongs to the low soil erosion hazard category. Out of the total land, 23.4% belong to moderate 18.0% is high, 7.8% very high, and 1.3% extremely high soil erosion hazard classes.
The InVEST SDR model is completely dependent on the USLE while USLE only represents the rill and inter-rill soil erosion process excluding mass erosion and gully erosion processes . However, mountainous and steeply sloped land areas might be significantly undergone mass erosion and gully erosion. Furthermore, in this model, the C and P factor values are incorporated respectively into the given LULC data