ABSTRACT
?
?ABSTRACT
Rainfall erosivity describes the capability of rainfall to cause soil erosion from a surface during a storm. Currently, the average long-term annual and monthly rainfall erosivity for a location may be obtained from isoerodent maps using a sample of climate stations across the United States (US). However, at the state-scale these measures are sporadic, using rainfall data from an increasingly outdated period, which may not reflect possible changing rainfall erosivity. Focusing on the state of Kentucky, US, we developed rainfall erosivity grids from the state Mesonet system to determine if erosivity has changed spatially and temporally between the original erosivity datasets from the latter twentieth-century and 2011–2020. We further used the Mesonet-derived dataset to develop a GIS-based model to estimate potential soil erosion for croplands during the fallow-season when soil is most prone to erosive storms. Results indicated that monthly erosivity increased across the state between the two periods. Larger increases in erosivity during the latter part of the fallow-season resulted in higher estimated soil erosion from croplands, particularly to the west. These findings may provide guidance in identifying and targeting croplands at greatest potential risk of soil erosion during the fallow-season for additional monitoring, mitigation and management practices.
Introduction
Croplands are highly vulnerable to soil erosion as many farming practices remove protective vegetative cover preceding the fallow-season (Kertis & Iivari Citation2006; Prasuhn, Citation2022). Areas left fallow following the harvesting of crops are especially prone to increased detachment and transportation of soil particles caused by rainfall runoff/splash (Panagos et al., Citation2016). This will initially occur as surface flow in the form of sheets and rills of water flowing across the soil surface, entraining the loosened soil particles (Govers et al., Citation2004; NRCS, Citation2012; Valentin et al., Citation2005). Recent soil erosion from cropland areas resulting from sheet and rill processes has been declining across the US on average, from an estimated 1.63 billion tons in 1982 to 0.98 billion tons by 2017, a decrease in rate from 9.6t ha−1 yr−1 to 6.6t ha−1 yr−1 (NRCS, Citation2020). However, there are still locations across the US with soil erosion rates that far exceed these declining national averages, highlighting that sheet and rill-based soil erosion remains a serious issue (NRCS, Citation2020). Lower soil erosion losses can still prove detrimental to croplands overall as a function of the soil loss tolerance, which describes the maximum rate of annual soil erosion that may occur and still permit a high level of crop productivity to be obtained economically and indefinitely (Schertz & Nearing, Citation2006; Weischmeier & Smith, Citation1978). The NRCS (Citation2019) determines a maximum tolerance of 12.5t ha−1 yr−1 for deep, well-developed soils more favorable to crop growth.
Sheet and rill-based soil erosion is directly controlled by the delivery of rainfall over a fixed period. Rainfall erosivity describes the ability of rainfall to loosen soil particles, particularly on slopes, during a rainstorm event (Nearing et al., Citation2017). During a rainstorm, the erosivity quantifies the effect of raindrop impact on the soil surface as a function of rainstorm energy per unit time (Nearing et al., Citation2017; Renard et al., Citation1997). Average annual rainfall erosivity values were first calculated for the eastern US from a series of climate stations for the period 1935–1957 to generate an isoerodent map in which lines of equal average annual rainfall erosivity values were drawn, used as an input to the Universal Soil Loss Equation (USLE) (Weischmeier & Smith, Citation1978). These were subsequently altered in the revised version of USLE (RUSLE) using a later time period of 1960–1999 to update the rainfall erosivity maps and provide additional average long-term monthly estimates of rainfall erosivity (USDA, Citation2013). Hollinger et al. (Citation2002) further updated rainfall erosivity data and examined some trends in seasonal and annual rainfall erosivity for multiple stations across the US using the period 1971–1999. They noted that increases in seasonal erosivity were apparent across several US regions, due to increasing rainfall during this later period. These findings also coincide with an increase in the number of heavy downpours (defined as the heaviest 1% of all daily events) occurring across these regions (Melillo et al., Citation2014). As higher temporal resolution rainfall data has become more available globally, many studies have since analyzed rainfall erosivity at a variety of timescales for spatial/temporal trends (Borrelli et al., Citation2016; Fenta et al., Citation2017; Liu et al., Citation2020; Nearing et al., Citation2015; R. McGehee & Srivastava, Citation2018; Schmidt et al., Citation2016) and seasonal influences (Angulo-Martinez & Begueria, Citation2012; Bezak et al., Citation2021; Lu & Yu, Citation2002; Wang et al., Citation2017; Yin et al., Citation2017).
The importance of rainfall erosivity in influencing soil erosion as well as other environmental factors has led to further research exploring the possibility of developing statistical relationships between rainfall erosivity values and corresponding rainfall depths over the same period. This research is driven by the fact that high temporal resolution (i.e. sub 30 minute) rainfall data, required for erosivity calculations, may be sparse in some areas, or will contain large portions of missing or erroneous data (Petkovsek & Mikos, Citation2004; Yin et al., Citation2015). For annual rainfall erosivity, the Fournier climate index has been successfully applied to estimate annual rainfall erosivity from monthly rainfall data (Biasutti & Seager, Citation2015; Nearing, Citation2001), while power regressions have been similarly applied on a monthly timescale (Bhuyan et al., Citation2002; Petkovsek & Mikos, Citation2004; Yin et al., Citation2015). Once developed, these statistical relationships can be used to interpolate point values of rainfall erosivity taken from high temporal resolution rain gauges to gridded precipitation datasets, thus providing continuous coverage of rainfall erosivity at a variety of spatial scales (Biasutti & Seager, Citation2015; Panagos et al., Citation2016; R. P. McGehee et al., Citation2022).
In this paper, we take a GIS-based approach to estimate the risk of fallow-season soil erosion for croplands across the state of Kentucky, USA, by interpolating monthly rainfall erosivity distributions for the period 2011–2020. In doing so, this research will address gaps in the current literature concerning rainfall erosivity and resulting impacts on soil erosion, much of which has focused on calculating annual values interpolated to national scales, over older time periods. As soil erosion is greatly influenced by changing vegetation cover, particularly over croplands during the fallow-season between the late Fall harvest and mid-Spring planting schedules, a monthly temporal resolution of rainfall erosivity interpolated using an increased density of local statewide rain gages is far more appropriate. Our research questions are as follows: (1) How does 2011–2020 fallow-season rainfall erosivity vary spatially across the state? (2) Has 2011–2020 fallow-season rainfall erosivity significantly changed from previous baselines used? (3) What is the estimated soil erosion from croplands during the fallow-season based on interpolated 2011–2020 monthly rainfall erosivity? Such findings may provide guidance in identifying and targeting croplands at greatest potential risk of soil erosion during the fallow-season for additional monitoring, mitigation and management practices.
Materials and methods
Study area
Kentucky has a distinctive East-West physiography beginning with the Eastern Coalfields/Cumberland Escarpment covered by mixed-mesophytic forests over narrow ridges and valleys with extensive surface coal mining occupying the easternmost part of the state (Figure 1). Moving westwards into the Bluegrass and Eastern Pennyroyal regions, lower-lying plains are dissected by uplands and knobs with areas of karst and bottomland forests replaced by cropland and pasture. Towards the west, the Western Coalfields/Pennyroyal, and Purchase regions present low-lying well-drained areas now fully dominated by croplands (Woods et al., Citation2002). The state experiences a humid-subtropical climate with mean annual precipitation ranging from 38–58 inches (~965-1475 mm), mainly distributed in the winter-spring season, and further projected to increase under multiple climate scenarios (Runkle et al., Citation2022).
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10 March 2023Figure 1. Physiographic regions of Kentucky and basic elevation cross-profile. Inset map displays Kentucky within contiguous USA.
Figure 1. Physiographic regions of Kentucky and basic elevation cross-profile. Inset map displays Kentucky within contiguous USA.
The two major crops grown in Kentucky are soy and corn, each accounting for approximately 6300 km2 area and generating a combined 32% cash receipts for the state (Citation2022a; USDA, Citation2022b). Both crops follow a similar single-season schedule, with planting typically beginning in late April-early May before harvesting is completed by early November (USDA, Citation2010). A smaller area of the cropland given over to soy and corn is further used for winter wheat, but the majority is left fallow between November and April. Owing to their economic importance to the state, we focus on the single-season corn and soy croplands in this study.
Monthly rainfall erosivity
The rainfall erosivity (R) of a location for a defined period may be calculated as follows:
(1)
(1)
where E = the total rainfall kinetic energy, I30 = the max 30-minute storm intensity for rainstorm event i, (EI30 MJ mm ha−1 hr−1) and j = the number of rainstorms in defined period (e.g. daily, monthly, annually etc). An individual rainstorm event is typically defined as a period of rainfall greater than or equal to 12.7 mm with a minimum of 6 hours of zero rainfall preceding and proceeding each event (Renard et al., Citation1997). E is duly calculated as:
(2)
(2)
where im = the rainfall intensity for each measured period of rainfall (mm hr−1). Consequently, rainfall data with a temporal resolution of at least 30 minutes must be available to calculate the erosivity for a location, data which is not widely available for most standard climate stations across the US. We obtained 5-minute rainfall data from the Kentucky Mesonet system (Kentucky Mesonet 2020). Currently there are 71 stations located across the state within this system, the earliest of which began operation in mid-2007. To take advantage of the greatest number of stations across a uniform period of 2011–2020 with less than 1% missing records, a total of 50 stations were utilized in this research. This station selection also provided relatively even coverage of the state across climate divisions and various environmental/topographic conditions for interpolation purposes (Figure 2). The Mesonet system is favored over the NOAA/NCDC stations originally used in calculating erosivity baselines as they provide a better spatial and temporal coverage of unbroken records of rainfall at a higher temporal resolution (5 vs 15 minute rainfall data) for Kentucky.
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10 March 2023
Figure 2. Kentucky climate divisions and Mesonet stations within counties.
We used the Rainfall Intensity Summarization Tool (RIST) available from the U.S. Department of Agriculture Research Service (USDA, Citation2019) to calculate individual rainstorm erosivity across the study period at each station. RIST allows users to enter rainfall data on a station-by-station basis at temporal resolutions between 1 and 30 minutes, before generating the total rainfall, duration, intensity, kinetic energy and erosivity of each storm. We applied the standard definition of an erosive storm event as requiring a minimum period of 6 hours separating each storm event for a minimum total depth of 12.7 mm as defined by RUSLE (Renard et al., Citation1997). Individual rainstorm erosivities were then summed by month for total monthly rainfall erosivity for the typical cropland fallow period (Nov–Apr). The corresponding long-term mean monthly erosivity values for each station coordinate (using the older baseline of 1960–1999) were obtained from the U.S. Environmental Protection Agency Rainfall Erosivity Calculator (Citation2022; US EPA, Citation2012).
Monthly rainfall erosivity interpolation
The PRISM Climate Group (Citation2020) provided monthly gridded rainfall data for 2011–2020. PRISM (Parameter-elevation Regressions on Independent Slopes Model) is a climate analysis system that uses point data from thousands of weather stations and cooperatives across the US, a digital elevation model (DEM), and other spatial datasets to generate gridded estimates of annual, monthly and event-based climatic parameters at resolutions of 800 m2 − 4 km2 (Daly et al., Citation2008). As a result, influences of elevation and topography are taken into account in the interpolated gridded output. From this dataset, we generated power regressions using the monthly gridded rainfall (PRISM) extracted from each Mesonet station grid cell as the independent, and monthly erosivity as the dependent variable to predict the monthly erosivity for each fallow season month (Bhuyan et al., Citation2002; Petkovsek & Mikos, Citation2004; Yin et al., Citation2015).
Estimating fallow cropland soil erosion risk
Various soil models have been developed in order to evaluate soil erosion towards adopting more effective mitigation practices in the short and long term. One of the most widely applied is the Universal Soil Loss Equation (USLE), originally based on empirical measures of soil erosion at research stations across the US (Weischmeier & Smith, Citation1978) and since revised as the RUSLE. The equation takes the form:
(3)
(3)
where A is the average soil loss for the area per unit time, R is the rainfall erosivity, K is the soil erodibility, L and S are the length and slope factors, C is the cropping factor, and P is the conservation practice factor. After calculating the rainfall erosivity (R), the rest of the factor values are based on losses from the same rainfall erosivity comparable to the standard unit plot of bare tilled soil, 22.1 m by 1.83 m in area, on a 9% slope (Weischmeier & Smith, Citation1978). Advances in GIS processing and availability of relevant geospatial datasets has further allowed the RUSLE factors to be calculated and analyzed over larger areas for use in GIS-based modeling (Behera et al., Citation2020; Benavidez et al., Citation2018). Starting with the K factor, which estimates the amount of soil loss as a function of various soil properties (including texture, structure, organic matter and permeability), we obtained erodibility and soil loss tolerance values by soil type for Kentucky in a 30 × 30 m gridded format from the NRCS state soil survey (Citation2022) (). The L and S factors are usually combined to account for the topographic controls of soil loss from surface runoff during rainstorm events, with various formulas devised based on local-to-regional landscapes and rainfall characteristics (Benavidez et al., Citation2018). Using the US-derived Moore and Wilson (Citation1992) formula as follows:
(4)
(4)
where As = flow accumulation area, B = slope, and p and q are empirical exponents of 0.6 and 1.3 respectively, we calculated the combined LS factor after generating flow accumulation and slope grids from a 30 × 30 m digital elevation model (DEM) as inputs. Finally, the cover (C) and support practice (P) factors represent land cover and any management applications both given as the ratio of soil loss with a particular cover and management practice in place to control soil erosion, such as contour tilling/planting and terrace systems (Alewell et al., Citation2019). The C factor is particularly relevant for cropland as a reduction in vegetation cover during the fallow season will result in increased soil erosion, particularly during wetter periods (Prasuhn, Citation2022). The C factor also depends on crop residue left on the field following harvest, which will limit soil erosion if left in place throughout the fallow season and subsequent spring crop tilling/planting (Alewell et al., Citation2019; Panagos et al., Citation2016; Santhi et al., Citation2006; Weischmeier & Smith, Citation1978). Based on this, we applied a C factor value of 0.23 for the months of November–March as crop stubble/residue is typically left following fall harvesting of single-season corn and soy, increasing to 0.31 for April when Spring tillage for the new seedbeds usually occurs (Weischmeier & Smith, Citation1978). For the final P factor, we applied values ranging from 0.5 to 0.9 to the slope grid for slopes between 1% and 25% to reflect contour tillage and planting, the most common form of cropland practice management in the US (Weischmeier & Smith, Citation1978).
Corn and soy croplands were identified across the state using the USDA National Agricultural Statistics Service (NASS) 2020 cropland data layer. This program uses satellite imagery on an annual basis to produce digital, crop specific, categorized geo-referenced gridded outputs of cropland cover aggregated to a maximum of 85 standardized categories (USDA, Citation2021). Individual crop types are classified, including single season corn and soy plantings, along with corn and soy with winter wheat plantings at a 30 × 30 m resolution for Kentucky. For the purposes of this study, we extracted the single season corn and soy cropland areas in which a Nov–Apr fallow-season would be expected to increase potential soil erosion over this period, to apply the RUSLE method. Total soil loss (t ha−1) for each month of the fallow season was then generated by multiplying together the gridded datasets for each factor in ArcGIS resulting in 30 × 30 m resolution monthly soil loss grids for the extracted corn and soy cropland locations within Kentucky. In order to better quantify and visualize the locations of greater estimated soil erosion across the state’s corn and soy croplands, we took the sum of the total soil erosion from corn and soy cropland within each corresponding HUC-10 (hydrologic unit code) watershed and divided by the area of the HUC-10 corn and soy cropland to give the mean estimated cropland watershed soil erosion.
Results and discussion
2011–2020 spatial monthly erosivity
Examining the 2011–2020 monthly fallow season erosivity by Mesonet station grouped by climate division identifies a decreasing west-east gradient, specifically between the two westernmost and easternmost divisions (Figure 3). November through March displays similar ranges and variances in erosivity, with slight reductions in the mean erosivity in January across all divisions. Moving into April, erosivity increases substantially, with mean values doubling at a minimum across all divisions, with the largest increases occurring in the Bluegrass division (Figure 3). Regarding the frequency of erosive storm activity across the state, Figure 4 shows the tally of the higher erosivity (upper quartile) storm events by climate division.
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10 March 2023Figure 3. Boxplots of monthly fallow season erosivity by climate division, 2011–2020. All units in MJ mm ha−1 hr−1.
Figure 3. Boxplots of monthly fallow season erosivity by climate division, 2011–2020. All units in MJ mm ha−1 hr−1.
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10 March 2023Figure 4. Upper quartile (Q4) 2011–2020 erosive storm counts by month and climate division.
Figure 4. Upper quartile (Q4) 2011–2020 erosive storm counts by month and climate division.
All divisions displayed a similar pattern moving through the fallow season, with a reduction in the number of high erosivity storms in November and January, with a smaller peak in December and maximum events in April. A clear decreasing west-east gradient is also present again, with fewer of these events occurring eastwards across the fallow-season. The spatial and temporal pattern of monthly erosivity across Kentucky is clearly influenced by the meteorological and climatological processes experienced by the southeastern US region which deliver moisture to different areas of the state from differing sources and at different time-scales (Kunkel et al., Citation2013). In particular, frontal systems moving in from the northwest will dominate the western side of the state beginning in the early winter resulting in wetter conditions and increased erosivity as a result. Moving into early spring, additional slower-moving thunderstorms, driven on by secondary convective processes as temperatures begin to warm in the Central and Bluegrass regions, generate localized heavy rainfall and increasing erosivity at those locations (Kunkel et al., Citation2013). Elsewhere, increasing elevation (Figure 1) and the influence of orographic uplift towards the east does not lead to an increase in erosive activity.
Changes in monthly erosivity
Figures 5 and 6 display the change in monthly fallow season erosivity between the older national baseline period (1960–1999) and the Kentucky Mesonet period (2011–2020) at each climate station expressed as a percentage and as mean differences grouped by climate division.
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10 March 2023Figure 5. 1960–1999 to 2011–2020 % change in erosivity by station. Red circles indicate increases, blue indicate decreases.
Figure 5. 1960–1999 to 2011–2020 % change in erosivity by station. Red circles indicate increases, blue indicate decreases.
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10 March 2023Figure 6. Mean of erosivity differences between baseline and Mesonet periods for Bluegrass (a), Central (b), Eastern (c) and Western (d) climate divisions. All units in MJ mm ha−1 hr−1.
Figure 6. Mean of erosivity differences between baseline and Mesonet periods for Bluegrass (a), Central (b), Eastern (c) and Western (d) climate divisions. All units in MJ mm ha−1 hr−1.
further displays the median difference in mean monthly erosivity values between the two time periods by climate division, highlighting significant differences (p < 0.05) in the median values from a Mann–Whitney U-Test (owning to the non-normal distribution present in many of the erosivity datasets). Increases in erosivity dominated, particularly for April with the greatest mean of erosivity difference of 293MJ mm ha−1 hr−1 for the Bluegrass division and 156 and 161MJ mm ha−1 hr−1 for the Eastern and Western divisions respectively. Greater median increases also occurred during the month of April, with the exception of the Central division, offset by several stations recording small decreases in erosivity (Figure 5). Of the four divisions the Western division was the only one to record consistently higher mean erosivity differences across all fallow season months, ranging from 23-161MJ mm ha−1 hr−1 for March and April, respectively. The Western division was also the only division displaying significant increases in median erosivity across all months with the exception of March, and the only division to record increases in mean erosivity across all months.
Table 1. Difference in mean erosivity medians between old and new baseline periods by climate division.
These results indicate that rainfall erosivity has generally increased for many of the fallow-season months with the noticeable jump in April signifying a possible shift towards more erosive rainfall in spring. In particular, the Bluegrass Region displayed the greatest overall erosivity increases for April, with most stations logging increases in excess of 100% of the older baseline records. This further agrees with the notion of increasing erosivity driven by warming temperatures and increased moisture delivery that typically occur entering the spring season. As such it appears that the older baseline established for rainfall erosivity does not reflect the 2011–2020 erosive storm behavior across the state, and as a result applications of these older baseline values towards soil erosion estimation will likely underestimate soil losses.
Interpolated monthly erosivity
Developing power regressions, using the PRISM mean monthly 2011–2020 precipitation as the predictor to the corresponding monthly erosivity as the predictand variable, generated results with varying degrees of goodness-of-fit ( and Figure 7). For all fallow-season months the power regression b exponent ranged between 2.46 and 2.92 similar to values generated by other studies applying both theoretical and empirical monthly rainfall distributions when predicting corresponding monthly erosivity (Petkovsek & Mikos, Citation2004).
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10 March 2023Figure 7. Observed vs simulated erosivity by month based on monthly PRISM precipitation, 2011–2020. All units in MJ mm ha−1 hr−1.
Figure 7. Observed vs simulated erosivity by month based on monthly PRISM precipitation, 2011–2020. All units in MJ mm ha−1 hr−1.
Table 2. Power regression b exponents and key goodness-of-fit statistics for 2011–2020 monthly interpolated erosivity from corresponding PRISM precipitation grids. RRMSE units in MJ mm ha−1 hr−1.
December and January displayed higher relative root mean square error (RRMSE) and larger negative corresponding percent bias (PBIAS), suggesting a greater under-fitting of observed erosivity values for the resulting regression models. The underestimation for these two months occurs for middle-higher range winter erosivity values (300–500 Mj mm ha−1 hr−1), suggesting that the monthly rainfall datasets do not truly capture the more intensive winter erosive storm events. Ballabio et al. (Citation2017) found a similar outcome for weaker model performance for summer erosivity using monthly rainfall interpolated across Europe. Spring months of March and April saw better model performance with lower RRMSE and PBIAS values. Despite this range in model performance by month across the fallow season, the RRMSE values all fell at or below 16% and, with the exception of January, PBIAS less than 10% suggesting a good overall fit between the observed and modeled erosivity values developed from the monthly PRISM precipitation data.
Normalized interpolated monthly erosivity grids for the 2011–2020 period are presented in Figure 8 along with the corresponding PRISM precipitation. November through January shows lower precipitation across the state and lower ranges in erosivity as a result (362–380 MJ mm ha−1 hr−1), before significantly increasing between February and April (438–764 MJ mm ha−1 hr−1). Greater precipitation clustered around the northern part of the Bluegrass, Central and southern Western climate divisions results in peak erosivities at those locations in April. This also corresponds to the increasing number of highly erosive storms recorded at these locations, with much reduced storm activity further eastwards during the 2011–2020 period (Figure 4), and greater increase in erosivity from the older baseline period (Figure 5).
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10 March 2023Figure 8. Fallow season monthly 2011–2020 PRISM rainfall and normalized interpolated erosivity.
Figure 8. Fallow season monthly 2011–2020 PRISM rainfall and normalized interpolated erosivity.
Estimated fallow season soil erosion risk
Incorporating the interpolated rainfall erosivity grids in tandem with the length-slope (LS), soil erodibility (K), cover (C) and support practice (P) factor grids into the RUSLE equation in ArcGIS, we generated soil erosion estimation grids for the corn and soy croplands on a statewide basis averaged within each corresponding HUC-10 watershed on a bi-monthly basis (Figure 9).
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10 March 2023Figure 9. Percent corn/soy cropland by HUC10 (a), estimated bimonthly fallow season cropland mean soil erosion grouped by HUC10 watershed (units in t ha−1) for Nov/Dec (b), Jan/Feb (c), and Mar/Apr (d).
Figure 9. Percent corn/soy cropland by HUC10 (a), estimated bimonthly fallow season cropland mean soil erosion grouped by HUC10 watershed (units in t ha−1) for Nov/Dec (b), Jan/Feb (c), and Mar/Apr (d).
Watersheds in which less than 10% area were accounted for by corn and soy cropland were excluded, mainly occurring to the eastern and some central portions of the state with increasing elevation and topographic range, less suitable for crop growth (Figure 1). As a function of the temporal distribution of rainfall erosivity previously described, there is a clear increase in the estimated soil erosion losses in March/April, with similar, yet lower soil erosion estimations between November/December and January/February (Figure 9). Earlier fallow-season soil losses are all estimated below 3t ha−1 across all watersheds, located in the far Western climate division as a function of slightly increased rainfall erosivity westwards. Moving into March/April, estimated soil losses significantly increased in size and spatial coverage across the state. Maximum estimated watershed soil losses peaked at 5.3t ha−1 during March/April, located in the Western climate division and northern section of the Bluegrass division due to greatly increased rainfall erosivity, particularly in April (Figure 8). An increase in the cover (C) factor for April as a function of spring tillage preparing the ground for new seeding also served to increase estimated soil losses across the state, up from earlier fallow-season months in which crop residue is typically left in place to form some protection from sheet and gully erosion. Topography also appears to play more of a minimal role in estimated soil erosion, with much of the higher estimates occurring in the Western and Central divisons where topographic relief is reduced (Figure 1). Significantly, this means that fallow corn and soy croplands, particularly in the western portion of the state, are potentially at a much elevated risk of soil erosion at a time when more erosive storm activity is also expected to occur. Consequently, techniques that manage soil erosion should be targeted at these locations during this later fallow-season period to achieve the greatest potential impact on soil loss reduction.
Figure 10 shows the total fallow season estimated cropland soil erosion in comparison to the annual soil loss tolerance (T) as determined by the NRCS (). Maximum total erosion estimates over 8t ha−1 over the fallow season are clearly focused across a cluster of five watersheds to the western extremity of the state, also accounting for the watersheds with the greatest proportion of cropland coverage (Figure 9). While annual soil loss tolerances for Kentucky are generally high, ranging between 8 and 13t ha−1 yr−1, the western section corresponding to these higher estimated erosion rates includes lower-mid tolerances ranging 9-10t ha−1 yr−1. When factoring in the higher estimated fallow season soil erosion at this location, the resulting proportion of the soil loss tolerance is very high, at over 90%. When further factoring in that these soil erosion estimates are only for the fallow season (Nov–Apr) compared to the full 12 months for the annual soil loss tolerances, these ratios take on a greater significance. While soil erosion losses would expect to be reduced during the non-fallow season for croplands, any subsequent erosion during these times could potentially see these locations exceed their soil loss tolerances. This further supports the development of techniques that manage cropland soil erosion within these watersheds during the fallow-season period to reduce the soil erosion/tolerance loss ratio.
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10 March 2023Figure 10. Corn/Soy cropland total fallow season estimated soil erosion (top), annual soil loss tolerance (middle) and ratio (bottom), grouped by HUC10 watershed.
Figure 10. Corn/Soy cropland total fallow season estimated soil erosion (top), annual soil loss tolerance (middle) and ratio (bottom), grouped by HUC10 watershed.
Conclusions
Soil erosion losses continue to be a serious issue across the US, particularly for states in which a significant proportion of crops grown results in minimal protective cover during the fallow season. When this fallow season coincides with an increase in seasonal erosive storm activity, such as the late winter/early spring period across the southeastern US, the risk from soil erosion further increases. Based on the findings of this research, rainfall erosivity activity appears to have significantly increased over much of Kentucky, particularly across the western and north-central areas of the state towards the latter part of the typical fallow-season period (March/April). The overriding result is an increase in the estimated soil erosion for these croplands by as much as 5t ha−1 with some areas potentially approaching annual soil loss tolerances. The findings further suggest that the older baseline of erosivity is potentially outdated and no longer truly reflects the conditions present either spatially or temporally across the state.
This research also indicates that a GIS-based model adopting the RUSLE approach can be employed to estimate potential fallow season soil erosion for croplands using interpolated erosivity and other gridded datasets that correspond to the surface and drainage characteristics present. By identifying locations estimated at greater risk of elevated soil erosion at certain times as a function of greater erosive activity at a sub-watershed scale, best management practices (BMP) targeting these specific locations may be drafted by relevant managers and policymakers. Additional work examining other crop types and rotation practices could further contribute to this research. While beyond the scope of this study, scenarios that reflect climate change modifications to rainfall erosivity and changes in cropping and management practices during the fallow season (specifically the C and P factors of the RUSLE formula) could also be incorporated into any additional GIS-based modeling towards developing future BMPs here or elsewhere across the US.
| Division | Nov | Dec | Jan | Feb | Mar | Apr |
|---|---|---|---|---|---|---|
| Bluegrass | 22 | 69 | -27 | 47 | -28 | 256 |
| Central | 40 | 92 | 16 | 25 | −37 | 2 |
| Eastern | 8 | 51 | -21 | 32 | −30 | 160 |
| Western | 48 | 112 | 86 | 60 | −14 | 139 |
Note: Bold = significant (p < 0.05) increase, italic = significant (p < 0.05) decrease based on Mann-Whitney U-Test output. Units in MJ mm ha−1 hr−1.
| Month | b | RRMSE | PBIAS |
|---|---|---|---|
| Nov | 2.91 | 0.11 | 2.9% |
| Dec | 2.74 | 0.16 | −6.5% |
| Jan | 2.92 | 0.16 | −14.4% |
| Feb | 2.46 | 0.14 | −0.9% |
| Mar | 2.47 | 0.09 | 0.6% |
| Apr | 2.83 | 0.11 | −5.5% |
Disclosure statement
No potential conflict of interest was reported by the authors.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author, [CAD] upon reasonable request.