watershed sedimentation

Consider an environment at a particular location. The soil is characterized as silt loam with 3% organic matter and has a fine granular structure with moderate permeability. The local hill is 110 ft. long with 5-15% gradients. Corn agriculture provides 3 ft. of canopy for the underlying soil. Conventional tillage is used in the direction of the land slope.

Calculate Soil Loss from the USLE Equation (A = RKLSCP)

A = R K L S C P

A: Computed soil loss per unit area

R: Rainfall and runoff factor (or the Rainfall Erosion Index)

K: Soil erodibility factor, the soil loss rate per erosion index unit for a specified soil

L: Slope-Length factor, ratio of soil loss from the field slope length

S: Slope-Steepness factor, ratio of soil loss from the field slope gradient

C: Cover and management factor

P: Support practice factor, ratio of soil loss with a support practice (contouring, stripcropping, terracing with straight-row farming up and down slope, etc.)

A = (R:~185) (K:0.37) (LS:0.56 to 12.58) (C: ~0.14) (P: o.55 to 0.7)

Where our R value was estimated to be ~185 from the average annual values of the rainfall erosion index figure in Wishmeier and Smith. K is estimated to be ~0.37 from a table given by mepas.pnnl.gov. LS estimated from equation for 5% gradient and 15% gradient. C estimated from table outlined in Appendix A: Estimating Soil Loss with the USLE from Purdue.

LS = [(65.41 x S²)/(S² + 10,000) + (4.56 x S)/(√S² + 10,000) + 0.065] x (d/22.1)0.5

Where S is the % slope and d is the slope length in meters. For 5% grade, I found an LS value of 0.56 and for the 15% grade, 12.58. These values vary considerably from the Topographic Factor (LS) charts found in Renard (1997) where I interpreted values of 0.7 and 2.5 respectively for the 5% and 15% grades.

Soil loss increases with increasing slope due to the greater volume of runoff accumulating on the longer slope lengths.

As stated in Renard (1993), pertaining to the LS factor of the USLE, soil loss is much more sensitive to slope steepness than to changes in slope length. The limitation of the USLE LS factor is that a slope estimated for the equation represents the whole hillslope condition and is often a poor representation of the non-uniform topography.

non-uniform hillslope profile impacts on soil loss

Erosion at a given location along a hillslope profile is a function of the distance from the origin of runoff and steepness at that location (Toy et al Ch. 2). A non-uniform profile is characterized by remote deposition, a process by which pockets of deposition occur downslope due to irregularities in the topography and ultimately reduces the amount of soil loss from reaching the bottom of the hill and decreases sediment yield overall. This is because the amount of sediment runoff detaches can vary over short distances with changes in surface slope. While uniform slopes generate fine particle soil loss, concave and convex slopes demonstrated a greater size distribution of eroded particles (Sensoy, 2014). In Chapter 2 of Toy et al, it’s stated that sediment delivered at the end of a convex slope is greater than that from the uniform slope. Soil at the top of hillslopes are a direct product of the parent material and bedrock lying immediately beneath, while soil profiles residing on lower parts of the hillslopes are a product of a greater time sequence of depositional and valley forming processes.

Cs-137 Applications for Erosion Rates

Caesium-137 is a sediment fingerprint and conversions of concentration found in the soil profile can provide an accurate date of deposition and give recent sedimentation rates. Cs-137 is a radionuclide fallout that has a half-life of 30 years and was released into the atmosphere in the 1950’s, 60’s and 70’s from nuclear weapon testing. It was distributed globally and related to local precipitation patterns (Ritchie, 1990). By comparing a local reference inventory, an area that hasn’t experienced erosion or deposition since Cs-137 was deposited, and those inventories measured at different points on a slope, we can identify eroding and depositional areas from different levels of the Cs-137 (Walling, 2006). For our soil that’s being cultivated for corn agriculture, it would be necessary to consider the effects of how tillage can redistribute or remove Cs-137 from the soil profile and into water (Ritchie, 1990).

Consider the environment at a particular location. The Watershed Area is 21 km^2. The Reservoir Area is 3.1 ha. The Average Depth of Deposition over four years is 27 cm. The Average Bulk Specific Gravity is 1.8. The Summer Trapping Efficiency (TE_summer) is 55%. The Other Trapping Efficiency (TE_other) is 21%. Sediment concentration measurements indicated 52% of sediment transport occurs in the Summer. Erosion problems are dominated by Bank Erosion. Lateral erosion for a 2.7 km segment is caused by Upland Urbanization. The average bank height for the 2.7 km segment is 1.4 meters.

Estimate Sediment Yield

Sediment Yield (SY) = [((ρsdsAs)/t) * 100]/(TE x Aw)

Where ρs is the bulk density, ds is the mean depth of the deposit, As is the area of the reservoir, t is time, TE is the trapping efficiency expressed as a percent, and Aw is the area of the watershed.

SY_summer = [(1.8 * 27cm * 3.1ha / 3 mo.) * 100] / (55% * 21km^2)

SY_other = [(1.8 * 27cm * 3.1ha / 9 mo.) *100] / (21% * 21km^2)

What is the Lateral erosion rate per lineal meter per year assuming SDR = 1?

Assuming the sediment delivery ratio (SDR) is equal to 1 means the sediment yield of the system is the same as the amount of soil loss.

seasonal variation in sediment loads (in terms of rainfall and transport processes) and how rainfall erosivity varies

Erosivity of rainfall (EI) is a product of total kinetic energy of a storm and maximum 30-minute intensity for a given storm. It’s the attempt to account for multiple mechanisms of raindrop impact (intensity) and fluvial detachment (net amount of rainfall). EI will vary spatially and seasonally depending on regional climatic regimes. Erosion is greatest when peak period of erosivity corresponds to the time when the soil is most exposed to raindrop impact and surface runoff. This period occurs when there is minimal ground cover from vegetation. Precipitation form is a function of temperature and strongly influences erosivity. Snowfall and rainfall on frozen soil produces no erosion but runoff from snowmelt and rainfall on thawing soils can produce very high erosion rates. Infiltration is a function of antecedent soil moisture. Runoff occurs when the rate of precipitation onto the surface is greater than the infiltration rate into the ground (Toy et al). Soil moisture increases during the months when rainfall is high and temperature is low due to reduced evaporation. Soil erodibility is usually higher during the late winter and early spring compared to in late summer. Erodibility is also reduced in the late summer because of biological activity increases in the soil and produces organic compounds which act as bonding agents. Organic matter is highly reactive in soil and provides surface area for water storage and binds mineral soil particles into larger aggregates that resist water and wind erosion. The structure of transported sediment aggregates is more irregular in the summer, while the winter produces more compacted and spherical sediments. These differences indicate biological processes rather than physical processes, given by increased heterotrophic bacteria decomposition levels (Fox, 2014). The surface area of transported sediment during the summer is estimated to be 40% greater than in winter. The increased surface area increases the sediment’s affinity to adsorb onto contaminants. Fox and Ford (2012) found a lower density of transported sediments in the winter and higher density of sediments in the summer which was indicative of carbon content (Fox, 2014).

Seasonal variation in trapping efficiency (TE)

Trapping Efficiency is defined as a percentage of the difference of the mass of sediment in and out of a reservoir normalized over the mass of sediment into the reservoir. Higher trapping efficiency can be expected in winter months when flow velocity of streams are lower, more suspended sediments are settling, and streams have a decreased transport capacity. Lower trapping efficiencies characterize summer months when there are high loads of suspended sediments in the streams. Increased turbidity levels are associated with high intensity precipitation events that generate a lot of runoff and carry more sediment into streams via rill erosion.

How does the SDR - sediment delivery ratio - impact the erosion rate?

Sediment deposition ratio is described as a watershed’s response to upland erosion, defined by the ratio of sediment yield at the outlet to the total sediment load generated by the watershed. Only a relatively small amount of sediment eroded within a drainage system will be transported all the way to the outlet (SY) due to deposition and storage processes.

High SDR values are correlated with high sediment erosion rates. As the transport capacity of a stream changes and sediment yield decreases, the SDR increases because the transport capacity of the stream increases and can therefore, deliver a greater mass of sediment to the outlet.

References

Fox, J., Ford, W., Strom, K., Villarini, G., & Meehan, M. (2014). Benthic control upon the morphology of transported fine sediments in a low‐gradient stream. Hydrological Processes, 28(11), 3776-3788.

Woznicki, S. A., & Nejadhashemi, A. P. (2013). Spatial and temporal variabilities of sediment delivery ratio. Water resources management, 27(7), 2483-2499.

Walling, D. E., Collins, A. L., Jones, P. A., Leeks, G. J. L., & Old, G. (2006). Establishing fine-grained sediment budgets for the Pang and Lambourn LOCAR catchments, UK. Journal of Hydrology, 330(1), 126-141.

Renard, K. G., & Ferreira, V. A. (1993). RUSLE model description and database sensitivity. Journal of environmental quality, 22(3), 458-466.

Sensoy, H., & Kara, Ö. (2014). Slope shape effect on runoff and soil erosion under natural rainfall conditions. iForest-Biogeosciences and Forestry, 7(2), 110.

Ritchie, J. C., & McHenry, J. R. (1990). Application of radioactive fallout cesium-137 for measuring soil erosion and sediment accumulation rates and patterns: a review. Journal of environmental quality, 19(2), 215-233.

Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., & Yoder, D. C. (1997). Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE) (Vol. 703). Washington, DC: US Government Printing Office.

Khelifa, A., & Hill, P. S. (2006). Models for effective density and settling velocity of flocs. Journal of Hydraulic Research, 44(3), 390-401.

Toy, T. J., Foster, G. R., & Renard, K. G. (2002). Soil erosion: processes, prediction, measurement, and control. John Wiley & Sons.

 watershed sedimentation

How does sedimentation impact aquatic ecosystems?

High sediment concentrations directly impact fish populations by reducing growth and resistance to disease as well as preventing sufficient egg development and affecting natural migration. Fish are indirectly impacted by high sedimentation due to a significant reduction in food sources. Deposition of sediment on the riverbed can harm the physical habitat of riffle-dwelling species by filling in spaces between larger substrate particles creating a smooth bed. This decreases the hydraulic roughness, increasing flow velocity and making the current even more vital.

Data used to monitor this relationship involved identifying relative fish abundance at various sites and calculating the associated fish diversity with Simpson’s D equation, as fish diversity is a good index for productive aquatic ecosystems. Relative levels of suspended sediment, turbidity, conductivity, and visual water clarity measurements were taken from water samples. Water depth and water column flow velocity over two cross sections were necessary to calculate an average flow discharge, depth, and velocity for the given stream. Measurements were also taken of stream slope, maximum depth, and channel widths. The amount of overhead cover such as an overhanging bank or in-stream debris was estimated visually as a percentage of the reach area. Median substrate size was also noted to infer the substrate stability index which suggests whether a bed is stable or unstable.

Agriculture contributes to high sediment loads in watersheds from soil erosion of the farmland whereby the erosional processes, runoff and wind erosion, impacts the fertile and most productive topsoil layer first.

methods for sampling stream channel sediment content:

Streamflow sampling method involves predicting sediment discharge with a heavy field measurement data emphasis.

Reservoir sedimentation survey method involves calculating reservoir trapping efficiencies by weighing sediment volumes collected from the site.

The sediment delivery method involves calculating a percentage-ratio for a given flow.

Empirical equations offer help in modeling theoretical flow situations. Methods based on equations are most useful in large watersheds where there are large amounts of eroded material. A simulated watershed model involves utilizing kinematic equations describing sediment transport in response to a particular runoff event.

Is Soil Moisture related to sediment delivery?

Soil moisture is not a good indicator of sediment delivery. As soil becomes compacted along a stream, it becomes more vulnerable to bank failure due to the decrease in cohesiveness and ability to supply water to stabilizing roots.

Grain Size Analysis

You’ve been given a sediment sample consisting of various grain sizes. Using a sieve, each distinctive grain size (phi) is separated, weighed, and listed as a percent weight of the whole sample before (weight%) and after (cumulative weight%) the sieving process.

Weight of Sample before Sieving: 657.5 g

Total Weight Retained: 647 g

Difference between original weight before sieving and total weight retained: 10.5 g

Percent Error = (D/WeightOriginal) * 100 = 1.597 %

The data are plotted. A histogram reveals the frequency curve represented in orange by Grain Size vs. Weight %. A cumulative curve with an arithmetic ordinate scale is produced in blue by Grain Size vs. Cumulative Weight %.

frequency plot: mode

The mode represents the phi size that is most prevalent or most common in the total grain population of the sample. The highest point on the histogram is the mode. Here, we found phi size 1.5 to make up the largest fraction of the total sample.

Cumulative Weight % Plot: median

Median = midpoint of the grain-size distribution

To calculate, the phi sizes must be in order from smallest to largest. Then, divide the number of terms (n) by 2 to give the nth term that will be the median. To solve with the graph of phi size vs. cumulative weight %, the median can be identified on the cumulative curve at the 50th percentile: 1.3 ϕ

Cumulative Weight % Plot: mean

Mean = arithmetic average of all the particle sizes in the sample

Values at the 16th, 50th, and 84th percentiles along the cumulative curve were averaged to find the mean.

M = (ϕ16 + ϕ50 + ϕ84) ÷ 3

= (0.7 + 1.3 + 1.95) ÷ 3

                = 1.31667 ϕ

Cumulative Weight %: Standard Deviation

Standard Deviation = measures the degree of sorting by giving a grain size range that reflects energy variations associated with the depositional process of the sample

σ = [ (ϕ84 - ϕ16) ÷ 4 ] + [ (ϕ95 – ϕ5) ÷ 6.6 ]

= [ (1.95 – 0.7) / 4 ] + [ (2.55 – 0.15) / 6.6 ]

                = 0.676

Cumulative Weight %: Skewness

Skewness = measures the cumulative curve’s degree of symmetry and may represent mixing of grain size populations

Sk = [ ϕ84 + ϕ16 – 2(ϕ50) ] / 2(ϕ84 - ϕ16) + [ ϕ95 + ϕ5 – 2(ϕ50) ] / 2(ϕ95 – ϕ5)

= [ 1.95 + 0.7 – 2(1.3) ] / 2(1.95 – 0.7) + [ 2.55 + 0.15 – 2(1.3) ] / 2(2.55 – 0.15)

                = 0.040833

Cumulative Weight %: Kurtosis

Kurtosis = measures the ‘peakedness’ of the distribution

K = (ϕ95 – ϕ5) / 2.44(ϕ75 – ϕ25)

= (2.55 – 0.15) / 2.44*(1.72-0.925)

                = 1.23724

Discussion of Results

The value of standard deviation, or sorting, was 0.676 indicating it was a moderately well sorted sample. The value for skewness indicated the sample’s grain size distribution had very high symmetry. However, because the mode value (1.5 ϕ) is slightly larger than the median and mean values (1.3 and 1.3667 ϕ), the distribution has a very slight negative or coarse skew. The peakedness of the distribution was found to have a 1.23724 kurtosis, characteristic of a leptokurtic curve where the grain sizes in the center of the histogram are better sorted than in the tails of the histogram.

Since this is sample represents a unimodal rock, the grains likely came from a siliciclastic rock. These data suggest a Wentworth ‘medium sand’ size class. It can be assumed that this sample came from a long system perhaps along a system of distributary channels in a relatively shallow alluvial setting. Therefore, the source area is likely a watershed and a result of upland erosion or streambank erosion.

Sources of error for dry sieving include not be an adequate process for measuring very fine material because the fine material particles have attractions between them that would require greater mechanical energy to pass through a mesh. Also, sieve analysis does not account for elongated particles of a certain diameter that don’t pass through a screen due to their elongate shape. These both minimize the distribution of the finer grain sizes.

 fluvial geomorphology

Define Threshold

In a geomorphic context, a threshold separates different states of a system, marking a transition in the behavior of that system; or a magnitude that must be exceeded for a resulting condition. 

Phenomena: Critical ThresholD

Hillslope stability: angle of internal friction

Saturation-excess runoff generation: Excess storm/precipitation water

Glacial mass balance: unequal annual net volumes of accumulation and ablation

Coastal wetland loss: rate of coastal submergence exceeding vertical accretion rate 

Fluvial channel erosion: transport capacity

difference between coastal submergence (or emergence) and eustatic sea level change

Cliff or Bluff Coastlines: Eustatic sea level change (or rise) is a global effect, while coastal submergence involves eustatic sea level change and also a factor of uplift. Cliff or bluff coastlines retreat due to a combination of wave action and mass wasting. Rising sea levels can submerge shore platforms allowing larger waves to reach cliffs and bluffs, accelerating the erosion and submergence of the coastal rocky cliffs. However, in some areas, uplift can partly offset sea level rise, so the effects of coastal submergence are less than the effects of eustatic sea level change.

Wave Refraction

The process of wave refraction is described as waves enter shallow water, they become more in contact with the air and sand surface which slows them down due to the greater exposure to friction. A portion of the wave entering the shallow water will move more slowly than that of on traveling in deeper water. By the process of wave refraction, waves align themselves with shore contours. This explains why waves always approach the shoreline approximately parallel no matter where they are in the world.

The Problem with Barrier Island Development

Barrier islands are geomorphologically unstable, separated from the mainland by estuaries and marshes, they respond to the build up of sand bars by drowning, or erosion and submergence, or landward migration by oceanside erosion and back barrier accretion. These processes result in a fluctuating shoreline, of which is often developed by high real estate property. Humans have constructed sea walls and implemented beach nourishment programs to prevent beach erosion. However, these methods are not permanent, need constant upkeep, are costly, and some examples of these can even exaggerate the effects of beach erosion. Rising sea levels also threaten the stability of barrier island development.

Karst

Diagnostic Landforms: Cavern systems, sinkholes, gorges, grikes, natural bridges.

These are generally  formed by water and wind erosion and the chemical weathering process of dissolution.

Periglacial

Diagnostic Landforms: Solifluction lobes.

These are created by a downslope creep of an active saturated layer over a permafrost surface.

Process + Force + Resistant + Method of Measurement

Glacial Scour of Bedrock:

                        Force: Depth of glacier, slope gradient, gravity

                        Resistance: rock structure, rock strength

This can be measured by analyzing the crushing force of a glacier, determined by a modified version of the shear stress equation: t = g·p·h·S......... Where t (tao) is shear stress, g is gravity constant (9.81), p is glacial ice density (900), h is glacier depth, and S is slope gradient.

Fluvial Sediment Entrainment:

                        Force: Gravity, channel slope

                        Resistance: Cohesion, size of particles

This can be measured by analyzing the stream power equation: Ω = p·g·Q·s ............ Where Ω is stream power, p is water density, Q is discharge volume, and s is channel slope. This could also be determined by evaluating the particle cohesion on the Hjulstrom curve, small particles can be kept moving because settling velocity is so low.

If the orbital diameter of a deepwater wave is 0.9 m at the surface, and if the wavelength is 20 m,

A.  What is the orbital diameter at a depth of 4 m?

DH = (Do/2) ^(H/(L/9))

DH = Orbital diameter at depth H

Do = Orbital diameter at surface

L = wavelength

            4 m  = (0.9/2)^(H/(20/9))

            H  = -3.858 m

B.  What is the depth of wave base?

Wavelength/2 = Wave base

20/2 = 10 m

A hurricane with a wave height of 8.5 m and a period of 18 seconds has how much more energy than a non-storm wave with H = 0.9 m and T = 7 sec. ?

E = CH2T2, where C is constant 17.

            E = 17(8.52)(182) = 397,953 J/m

            E = 17(0.92)(72) =  674.73 J/m

            The difference is 397,278.27 J/m.

Froude Number (Fr)

Fr = v/[(gd)*0.5]

            Fr > 1 Velocity faster than an ideal condition (rapid, supercritical)

            Fr < 1 Tranquil, subcritical

            Fr = 1 Critical Threshold

-          Least sensitive to friction

-          Correlates best with habitat

-          Less variable with flow conditions

Aquatic Habitat Affected by:

-          Velocity

-          Shear Stress

-          Stream Power

-          Discharge (Q)

-          Reynold’s Number (Turbulence)

Velocity of Flow

Velocity = f(R^aS^bf^-c)

            R = Hydraulic Radius = Cross-sectional area divided by wetted perimeter  (≈ mean depth)

            S = Slope

            f = Friction Coefficient

            Where a,b,c > 0

Reynold’s Number (Re)

            Re = pdV/ϒ

V = Velocity = (8gRS/f)0.5

p = density

d = depth (R ≈ d)

ϒ = Kinematic Viscosity

How does Slope change?

            Uplift, down-warping, cut & fill

How does Distance change?

            Base level, meandering (sinuosity = channel length / valley length)

How does Roughness change?

Shape, grain size, vegetation, channel irregularity, grain orientation, imbrication, embedded gravel, large woody debris or other debris, bars, bedforms

MMA = Multiple modes of adjustment

OFE = Opposite from expected (if increased flow greatly increases width, velocity could decrease)

Critical Shear Stress (Shield’s Function): Ʈ_CR

Ʈ_cr = kg(ρs-ρw)D

k = constant based on Shield’s Number (0.045)

ρs = density of sediment

ρw = density of water

D = medium particle diameter (mm)

Shear Stress ÷ Tractive Force = Flow Competence

Stream Power is proportional to Sediment Transport Capacity

Stream Power (Watts/m) of Channel Length

-          Rate of energy expenditure per unit time

-          Best measure of bulk sediment transport capacity

-          Specific Stream Power (power per unit bed width): ƮoV = yRSV

-          Cross-sectional Power (total stream power): Ω = yQS

-          Unit Stream Power (power per unit weight of water): VS = yQS/yA

2.2 Percent: The Outdoor Recreation Industry

September 24, 2018

 The people that support the Outdoor Recreation Industry may vote to conserve redwoods and public lands, but they don’t conserve the contents of their wallets. A recent report provided by the Bureau of Economic Analysis (BEA) brings the validation that men in tight biker shorts and women in harnesses with callused hands had been waiting for. The study found that Outdoor Recreation accounts for a considerable 2.2 percent of the nation’s GDP. This number was calculated by ‘Value Added’, a measure of an industry’s contribution to GDP, where

The value of the products and services produced – The value of the products and services used in production = the Value Added

Additionally, despite the millions of years of geologic time it took the parks to develop and establish their slow-growth forests, the Outdoor Recreation Industry out grew the U.S. economy with a 1.7% increase compared to country’s overall 1.6% increase in 2016 (BEA, 2018).

The report detailed that this 2.2% ($412 billion) contribution to the U.S. economy significantly outweighed those of the agricultural (1%) and mining industries (1.4%). However, recent thoughts suggest the mining industry’s projected growth will increase in the next few years as public lands such as Bears Ears and Grand Staircase-Escalante National Monuments are processed for their mineral resources.

The Outdoor Industry Association (OIA), a sponsor of the BEA’s report, is a member-operated trade organization that produces economic reports that present consumer behaviors, job sectors, and industry comparisons among those of outdoor recreation. The 2012 report found that more jobs are supported in the Outdoor Recreation Industry than there are in the Oil and Gas and Educational sectors combined, based on data from the 2011 Bureau of Labor Statistics report. The same issue states that annual consumer spending on outdoor recreation products and services accounts for twice as much annual spending than pharmaceuticals and gasoline and other fuels, based on data from the 2010 Bureau of Economic Analysis report.

The OIA’s latest and more inclusive 2017 edition of the Outdoor Recreation Economy documents the $887 billion in annual consumer spending, 7.6 million American jobs, and evolving demographics engaging in outdoor activities. The OIA reports that the Outdoor Recreation economic sector is greater than the Education, Gasoline and Fuels, Motor Vehicles, and Pharmaceuticals sectors and nearly matches the annual consumption of Outpatient Health Care ($931 billion). The recreation economy even generates a substantial $124.5 billion in federal, state, and local tax revenue annually. Furthermore, the OIA’s publication asserts that communities that invest in programs and infrastructure to support outdoor recreation improve education, reduce long-term health care costs, and lower crime rates.

A Local Note

As large of an industry outdoor recreation comprises, it effects the people and local economy in famous mountain cities like Boulder, CO and small hill towns like our Slade, Kentucky the same – a place where it’s easier to find moonshine and the elusive salamander than it is a light beer.

Both the BEA and OIA reports demonstrate that Kentucky has a profound opportunity to grow, more-so than ever before. The bluegrass state has more miles of navigable rivers and waterways than any other in the U.S. except Alaska. The OIA finds that there is greater spending on water sports gear ($14 billion) than on the annual sale of movie tickets ($11 billion). As our coal mines shut down, let’s not disturb our quiet Appalachian foothill towns with high speed internet, paved roads, and the best pain-killers east of the Mississippi. Instead, let’s offer jobs and healthier lifestyles with paddles and PFD’s.

Hopefully, the BEA’s report will validate the recreation industry’s efforts and encourage congress to reconsider the value and access our public lands provide in next year’s 2019 fiscal budget.

Outdoor recreation is no longer just about climbing trees and skipping stones. As long as people continue to be informed about environmental issues, to be innovative, to summit more physical and congressional terrain, and to always seek new adventures, our country will grow stronger in health and in the global market.

Additional Resources: Bluegrass Wildwater Association, Outdoor Industry Association, Bureau of Economic Analysis Updated Statistics Report Sept. 2018