U.S. Department of the Interior

U.S. Geological Survey

Southern Rockies Landscape Conservation 

Cooperative Unit Watershed Erosion Potential 

Prioritization for Check-Dam Installation

Open-File Report 2018–1127
Cover. Photograph of rock check-dam in an intermittent drainage adjacent to the Colorado River on the Palisades Delta in Grand Canyon National Park, 

Arizona, constructed by the Pueblo of Zuni in 1995. Approximately 30 centimeters of sediment were deposited behind the check-dam. (National Park 

Service photograph taken March 2005.)
U.S. Department of the Interior

U.S. Geological Survey

Southern Rockies Landscape Conservation Cooperative 

Unit Watershed Erosion Potential Prioritization for CheckDam Installation

By Kirsten E. Ironside

Open-File Report 2018–1127
U.S. Department of the Interior

RYAN K. ZINKE, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2018

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Suggested citation: 

Ironside, K.E., 2018, Southern Rockies Landscape Conservation Cooperative unit watershed erosion 

potential prioritization for check-dam installation: U.S. Geological Survey Open-File Report 2018–1127, 

15 p., https://doi.org/10.3133/ofr20181127. 

ISSN 2331-1258 (online)
iii

Acknowledgments

In addition to U.S. Geological Survey (USGS) support, funding for this work was provided by the Southern Rockies 

Landscape Conservation Cooperative through the Bureau of Reclamation's WaterSMART Program. I thank Jeffrey 

Lovich (USGS), David Dean (USGS), and Kirk Anderson (Museum of Northern Arizona) for helpful comments that 

improved the report. I thank Terry Arundel (USGS) for project oversight.
iv

Contents

Acknowledgments..........................................................................................................................................iii

Abstract ......................................................................................................................................................... 1

Introduction.................................................................................................................................................... 1

Methods......................................................................................................................................................... 4

Study Area ................................................................................................................................................. 4

Watershed Ranking.................................................................................................................................... 4

Results......................................................................................................................................................... 11

Region 1 .............................................................................................................................................. 11

Region 2 .............................................................................................................................................. 11

Region 3 .............................................................................................................................................. 11

Region 4 .............................................................................................................................................. 12

Region 5 .............................................................................................................................................. 12

Region 6 .............................................................................................................................................. 12

Discussion ................................................................................................................................................... 12

Conclusion................................................................................................................................................... 13

References Cited......................................................................................................................................... 13

Figures

 1. Study area map showing the Southern Rockies Landscape Conservation Cooperative 

 unit boundary and U.S. Geological Survey 12-digit hydrologic unit codes that intersect the 

 SRLCC boundary..................................................................................................................... 3

 2. Maps showing currently available Natural Resources Conservation Service soil type 

 erodibility by water index (K) for the area of the Southern Rockies Landscape Conservation

 Cooperative ............................................................................................................................. 6

 3. Maps showing spatial datasets used for developing the ranking system for Southern 

 Rockies Landscape Conservation Cooperative watersheds .................................................... 7

 4. Maps showing BioClim datasets used to develop similar climatic regions on which to rank

 Southern Rockies Landscape Conservation Cooperative watersheds..................................... 9

 5. Maps showing watershed ranking results for the six climatic regions in the Southern

 Rockies Landscape Conservation Cooperative unit............................................................... 10
v

Conversion Factors

International System of Units to U.S. customary units

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Abbreviations

BioClim bioclimatic variables from the WorldClim dataset

CDL crop data layer

CONUS contiguous United States

DEM digital elevation model

EPA Environmental Protection Agency

GeoWEPP Geo-spatial interface of the Water Erosion Prediction Project

HUC hydrologic unit codes

NASS National Agricultural Statistics Service

NCGC National Cartographic and Geospatial Center

NRCS Natural Resources Conservation Service

RUSLE Revised Universal Soil Loss Equation

SRLCC Southern Rockies Landscape Conservation Cooperative

gSSURGO Gridded Soil Survey Geographic database

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey

WBD Watershed Boundary Dataset
1

Southern Rockies Landscape Conservation Cooperative 

Unit Watershed Erosion Potential Prioritization for CheckDam Installation

By Kirsten E. Ironside

Abstract

Changes in land-use practices and the extirpation (local extinction) of beaver populations in the 

early 20th century during European settlement are believed to have resulted in many changes in how 

streams in the Western United States function. Some of the negative changes that have resulted include 

stream channelization, soil erosion, changing vegetation, water turbidity, and a loss of overland flow.

Efforts to restore streams and reduce soil erosion by water have included reintroductions of beaver, 

incorporating Native American traditional knowledge of dry-land farming techniques, and the 

installation of rigid check-dams. Many of these efforts have been successful in improving both 

intermittent and perennial stream function. Therefore, stakeholders in the Southern Rockies Landscape 

Conservation Cooperative (SRLCC) have identified a need to prioritize streams within their region of 

interest for the installation of check-dams to continue restoration and conservation efforts and to 

improve sediment catchment.

Using Natural Resource Conservation Service soil databases, topographic features derived from 

digital elevation models, stream networks, and regional climatic patterns, I developed a ranking system 

for watershed potential erosion rates and suitability for check-dam placement across the SRLCC. This 

ranking system serves as a first step for land managers to prioritize areas for check-dam installation 

based on relatively static factors (soil properties, topography, and hydrology) that can contribute to rates 

of soil erosion by water and the stability of check-dams. Many other relatively dynamic factors over 

time can contribute to rates of soil erosion by water, such as recent wildfire events, changes in weather 

patterns and extreme climate events, and changing land-use such as grazing, logging, mining, 

development, and cultivation. These factors that influence vegetative and biological soil crusts cover are 

also important elements to the potential erosion of soil by water. Because of this, SRLCC stakeholders 

might consider further evaluation of the watersheds identified here as high ranking. Final watershed 

prioritization among the high-ranking watersheds identified here should include current knowledge of 

land-use and land-cover estimates to identify areas at risk for soil erosion or degree of existing erosion 

problems.

Introduction

Erosion, the physical process where soil particles are moved from the ground surface by water or 

wind (Osterkamp, 2008), can result in lowered soil fertility and increased turbidity of runoff. Water 

erosion rates depend on many factors including precipitation intensity, soil characteristics, topography 

of terrain, and the cover of vegetation and biotic soil crusts. The most widely used model to predict soil 
2

erosion is the Revised Universal Soil Loss Equation (RUSLE), which consists of five factors—(1) a 

climatic parameter, the rainfall runoff erosivity factor (R), (2) a soil type erodibility by water index (K), 

(3) topographic metrics of slope steepness and length (LS), (4) land-cover type (C), and (5) the 

cultivation/conservation management factor (CP) (Renard and others, 1991). Whether precipitation is in 

the form of snow or rain and the degree of soil freezing, which are factors related to temperature, also 

influence erosion rates (Renard and others, 1997).

Previous research regarding rates of soil erosion by water have been primarily focused on 

agricultural contexts. Developing predictive soil-loss equations for other contexts is an active area of 

research currently. In the arid and semiarid regions of the southwestern United States, precipitation is 

often delivered by convective storm systems during the summer monsoon season, resulting in intense, 

heavy rainfall events that can result in flooding and channel erosion. Detention structures, such as 

check-dams (small dams across ditches or streams), have been shown to stabilize stream flow (Norman 

and others, 2016), retain sediment (Polyakov and others, 2014), increase soil moisture (Nichols and 

others, 2012), and improve riparian vegetation (Norman and others, 2014).

Check-dams are physical barriers constructed across channels meant to induce sediment 

deposition by slowing flow rates and decreasing channel gradient. Where runoff volumes and rates are 

relatively low, such as in intermittent streams where channel entrenchment or headcuts have not 

developed that require engineered structures, hand-built check-dams offer a low-tech approach to 

improving landscapes. By inducing deposition, check-dams can be used effectively for grade 

stabilization and erosion control, while also improving ecosystem function by redistributing water 

within a catchment. Retaining runoff at check-dam sites for longer time periods is expected to induce a 

feedback loop, where increased water resources lead to plant growth and increased vegetative cover 

results in increased infiltration and soil moisture (Nichols and others, 2012).

Concern over stream entrenching in the Western United States began in the early 20th century

(Bryan, 1925) and continues to be a land-management concern into the future with projected climate 

changes likely to increase rates of soil erosion by water (Sankey and others, 2017). Historically, the 

North American beaver (Castor canadensis) occurred throughout the Southern Rockies Landscape 

Conservation Cooperative (SRLCC) unit (fig. 1), even in the arid intermittent streams of the region 

(Gibson and Olden, 2014). Research on beaver dams has shown that they alter stream hydrology, 

sediment dynamics, nutrient cycling, and create lentic habitats, and as a result, they alter stream flora 

and fauna (Naiman and others, 1986; Naiman and others, 1988; Rosell and others, 2005). Although 

beaver populations have grown substantially since their near extirpation (Rosell and others, 2005), large 

areas of modeled suitable habitat are currently unoccupied and there is a growing interest in mimicking 

beaver dams as a resource management tool (Gibson and Olden, 2014). Similarly, historic Native 

American dry-land farming techniques also incorporated the building of dams made of brush and trees 

in ephemeral drainages for water catchment and erosion control. Zuni Indian alluvial-fan farming 

techniques have shown the ability to reduce arroyo cutting, increase the frequency of overland flow 

events, and reconnect channels to alluvial fans (Norton and others, 2002; Pederson and others, 2006).
3

Figure 1. Study area map showing the Southern Rockies Landscape Conservation Cooperative (SRLCC) unit 

boundary (black line) and U.S. Geological Survey 12-digit hydrologic unit codes (U.S. Geological Survey, 2017) 

that intersect the SRLCC boundary. The SRLCC unit is more than 127 million acres centered on the four corners 

region of Arizona, Colorado, New Mexico, and Utah, and also includes parts of Idaho and Wyoming (Southern 

Rockies Landscape Conservation Cooperative, 2018).
4

The SRLCC Steering Committee has identified streams as a focal resource (see Southern 

Rockies Landscape Conservation Cooperative, 2018, for more information on the SRLCC formation, 

goals, strategies, and focus). Because of the arid climate of the SRLCC region, water is a limited 

resource, and stakeholders have identified stream-ecosystem conservation and management as an 

important need. Stream-flow management is important for both natural-resource conservation and also 

for societal needs (for example storage and delivery of water for human use such as irrigation, 

hydropower, and municipal supply). To meet the need of watershed prioritization for check-dam 

installation across the SRLCC, I compiled data from the best available geographic datasets for soils, 

terrain features, and hydrologic networks. I developed a ranking system for watersheds based on soilslope gradient characteristics and particular landforms intersected by streams that are best suited for 

check-dam installation.

Methods

This section described the methods I used to develop a ranking system for watershed potential 

erosion rates and suitability for check-dam placement across the SRLCC. The section describes how I 

delineated the study area and determined the watershed ranking.

Study Area

The climate of the SRLCC unit is variable due to encompassing elevation and latitudinal 

gradients, resulting in different temperature ranges seasonally and diurnally. It also varies in 

precipitation seasonality, with the southern extent predominantly receiving summer precipitation from 

the North American monsoon and the northern part primarily receiving winter-storm precipitation, 

much of it occurring as snow in the higher elevation mountainous regions. High-elevation alpine 

systems occur in the Colorado Rocky Mountains and the mountain ranges of Utah. The SRLCC unit

also encompasses the Grand Canyon in Arizona, which at its lowest elevations consists of Mojave 

Desert vegetation communities. Therefore, many biomes from shrub lands, to woodlands, to various 

forest types are represented within the SRLCC unit.

The SRLCC boundary was used to intersect hydrologic units from the U.S. Geological Survey 

(USGS) Watershed Boundary Dataset (WBD) (U.S. Geological Survey, 2017). The WBD is a nationally 

consistent watershed dataset that is subdivided into six levels and is available from the USGS and the 

U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) National 

Cartographic and Geospatial Center (NCGC). The WBD contains the most current 8-digit, 10-digit, and 

12-digit hydrologic unit codes (HUC) available. I selected the smallest watershed delineation, the 12-

digit HUC that intersected the SRLCC boundary to define the study area for analysis (fig. 1).

Watershed Ranking

I obtained soil data from the NRCS Gridded Soil Survey Geographic (gSSURGO) database for 

Arizona, Colorado, Idaho, New Mexico, Nevada, and Utah using the USDA Geospatial Data Gateway 

(U.S. Department of Agriculture, 2017). The gSSURGO databases were created for use in national, 

regional, and statewide resource planning and analysis of soils data and can be readily combined with 

raster layers, including the National Land Cover Database (NLCD), the National Agricultural Statistics 

Service (NASS) Crop Data Layer (CDL), and the National Elevation Dataset (NED) (Natural Resources 

Conservation Service, 2017). I queried the gSSURGO databases for K (the soil water erodibility factor), 

which is a soil metric based on soil texture, chemistry, and organic matter content. NRCS provides two 

K metrics, one for the whole soil horizon that includes rock fragments, kwfact, and one for the fine soil 
5

(no rock fragments), kffact. As K increases, the more susceptible the soil is to water erosion. Each 

gSSURGO map unit polygon has a one to many relation with the factor fields for K. Each map unit in 

gSSURGO has one or more components, and each component has one or more soil horizons. To 

estimate K for a single map unit, I calculated the average K value across horizons. When horizon 

thickness values were provided, I used a weighted average by thickness of the soil horizon. When the 

soil horizon K was provided for the whole soil, kwfact was used as K; and when not provided, kffact was 

used was used as K. The average K across soil horizons for the soil component was then averaged again 

by a weighted average using the component percentage of the map unit to determine the final map unit 

K value.

SSURGO soil data are not currently available for the entire study area (fig. 2A). Coarser spatial 

resolution NRCS soil data are available for these regions, and map unit K has been previously compiled 

for the United States using data from Miller and White (1998) by the Pennsylvania State University Soil 

Information for Environmental Modeling and Ecosystem Management program (Pennsylvania State 

University, 2006). K for the whole soil was obtained from the contiguous United States (CONUS)

dataset by downloading the ArcINFO coverage in the Albers projection (Pennsylvania State University, 

2006) (fig. 2B). The map unit source data from the gSSURGO and CONUS datasets were converted to a 

90-meter (m) resolution raster dataset using the majority function in Esri ArcGIS version 10.3 software 

(Esri, 2013), which provided K estimates for the entire SRLCC except parts of the Grand Canyon below 

its rim (fig. 2C).

Slope gradients also play an important role in the potential for soil erosion by water (Miller and 

others, 2011; Pederson and others, 2006). I calculated slope in degrees using a ~30-m digital elevation 

model (DEM) (U.S. Geological Survey, 2009), resampled to the 90-m resolution of the K grids using 

bilinear resampling in Esri ArcGIS version 10.3 software (Esri, 2013). K was multiplied by slope and 

summed by watershed using ArcGIS Spatial Analyst toolbox zonal statistics, resulting in a relative 

index of the potential for erosion due to soil properties and slope for the 12-digit HUC watersheds (fig. 

3). Watersheds were selected that contained an intermittent or perennial stream identified using the U.S. 

Rivers and Streams dataset in ArcGIS (Esri, 2012), which consists of intermittent and perennial streams, 

washes, and arroyos that are connected to higher order streams and rivers. This dataset was clipped to 

the study area and a subset of the dataset created to only include feature types of “Stream/River.” Closed 

watersheds are considered here to be low priority for check-dam installation, and watersheds with 

streams that could transport sediments to rivers and reservoirs are only considered for ranking (fig. 3).
6

Figure 2. Maps showing currently available Natural Resources Conservation Service (NRCS) (2017) soil type 

erodibility by water index (K) for the area of the Southern Rockies Landscape Conservation Cooperative (SRLCC). 

A, Map showing soil data available for the contiguous United States (Pennsylvania State University, 2006). B, Map 

showing finer resolution Gridded Soil Survey Geographic (gSSURGO) data available for parts of the SRLCC (U.S. 

Department of Agriculture, 2017). C, Map showing data from Pennsylvania State University (2006) used to provide 

information for gaps in the gSSURGO data for K to cover the watersheds of the SRLCC except for parts of the 

Grand Canyon where no data are available from either dataset.
7

Figure 3. Maps showing spatial datasets used for developing the ranking system for Southern Rockies 

Landscape Conservation Cooperative watersheds. A, U.S. perennial and intermittent steams data (Esri, 2012). B, 

Slope gradient (degrees; from U.S. Geological Survey, 2009) multiplied by K, C, A landform classification system

(Manis and others, 2001). HUCs, U.S. Geological Survey 12-digit hydrologic units codes (U.S. Geological Survey,

2017).
8

Beaver dams and check-dams have been found to be most effective on particular landforms such 

as gentle slopes and alluvial fans, where the dams result in more overland flow events, result in streamflow stabilization, and can promote the growth of riparian or semi-riparian plant species. Dams in 

streams along steep slopes and narrow draws are often damaged and are less effective at catching 

sediment (Pederson and others, 2006; Rosell and others, 2005). I developed another index to identify 

stream check-dam suitability using a landform classification system. I used a 90-m resolution DEM and 

the Arc Macro Language script developed by Manis and others (2001) to define 10 landform classes. I 

also added another class by extracting and adding a water body class from resampled 30-m 

USDA/USGS LANDFIRE data (LANDFIRE, 2011) to show areas of frequent inundation (fig. 3D). I 

reclassified the landform raster, giving the classes of “gently sloping toe slopes” and “gently sloping 

ridges, fans and hills” a value of one and the remaining classes a value of zero. I converted the stream 

line features into a raster dataset and assigned perennial streams a value of two and intermittent steams a 

value of one based on the FCODE_DESC field. These two raster datasets were multiplied together in 

Esri’s Spatial Analyst raster calculator, resulting in a raster showing where streams traverse landforms 

classified as “gently sloping toe slopes” and “gently sloping ridges, fans and hills.” I used zonal 

statistics to sum this check-dam suitability raster by watershed, which results in an indication of the 

amount of area (number of cells) where check-dams would be suitable to install, weighting the sum 

towards perennial streams over intermittent streams.

The final steps in developing our watershed ranking system included multiplying the watershed 

potential for water erosion index (the sum of K × slope index per watershed, fig. 3C) by the check-dam 

suitability index. This resulted in an index quantifying a watershed’s potential for erosion and also its 

suitability for check-dam installation. Because the climate of the SRLCC is highly variable due to 

elevation and latitudinal gradients, there is a fair amount of variability in soil temperatures, degree of 

freezing, and precipitation. Soil temperature, the length of time a soil is frozen, and whether or not 

precipitation is in the form of rain or snow both impact erosion rates. I further subdivided the SRLCC 

into climatic zones based on isotherms and precipitation seasonality to identify watersheds that 

experience similar climatic patterns for the final ranking system. I used the BioClim dataset accessed 

from WorldClim (2017) to parse regions within the SRLCC of similar climates using the variables of 

precipitation seasonality (BIO15) and isothermality (BIO3). Precipitation seasonality is calculated using 

the coefficient of variation in precipitation. Isothermality is calculated by dividing the mean diurnal 

range (that is, the mean of monthly maximum temperature minus the minimum temperature) by the 

annual range of temperature (that is, maximum temperature of the warmest month minus the minimum 

temperature of the coldest month) times 100. BIO3 and BIO15 were multiplied together for the study 

area to create an index of climatic similarity across the SRLCC (fig. 4). Using equal interval 

classification and six bins to define distinct climatic subregions, I assigned the SRLCC 12th level HUC 

watershed to the six climatic regions. Final watershed ranking from highest priority to lowest was done 

by climatic subregion (fig 5).
9

Figure 4. Maps showing BioClim datasets (accessed from WorldClim, 2017) used to develop similar climatic 

regions on which to rank Southern Rockies Landscape Conservation Cooperative (SRLCC) watersheds. A,

Isothermality (BIO3, a metric of temperature ranges) and, B, precipitation seasonality (BIO15) were used to create, 

C, an index of how climate varies across the SRLCC.
10

Figure 5. Maps showing watershed ranking results for the six climatic regions in the Southern Rockies 

Landscape Conservation Cooperative (SRLCC) unit. Maps of watershed ranking based on erosion potential of soil 

by water and the landforms streams traverse for regions 1 through 6, respectively.
11

Results

Using the WBD’s (U.S. Geological Survey, 2017) smallest watershed designation, 12-digit 

HUCs, resulted in selecting 6,255 watersheds for the analysis of the SRLCC region. Of these 

watersheds, 4,442 watersheds were found to have intermittent or small perennial streams that are 

connected to the larger stream, river, and reservoir systems of the SRLCC. These watersheds were 

evaluated for their soil properties (Natural Resources Conservation Service, 2017, soil type erodibility 

by water index, K), slope gradients, and landforms traversed by streams for identifying watersheds that 

could benefit from check-dam installation (Ironside, 2018). I identified six climatic regions within the 

SRLCC that had similar temperature ranges and precipitation patterns. Region 1, consisting of 1,626 

HUCs, is located in the northern SRLCC and is characterized by high-elevation mountainous areas of 

Utah and Colorado, where precipitation is winter dominated and often comes in the form of snow

(Knowles and others, 2006). The streams of this region are characterized by springtime runoff from 

snow melt. Region 2 consists of the four corners area of the Colorado Plateau Province and contains 

2,175 HUCs. This region experiences a bimodal precipitation regime— (1) winter precipitation 

sometimes in the form of snow and (2) summer monsoon precipitation. Region 3 consists of 1,183 

HUCs and encompasses parts of northern Arizona, New Mexico, and the lower elevation part of the 

Rocky Mountains in Colorado and Wyoming. This region experiences relatively large temperature 

fluctuations and also a relatively large degree of variability in precipitation seasonality. Region 4, 719 

HUCs, consists of the lower elevation HUCs that include the SRLCC parts of Arizona, New Mexico, 

Colorado, and Wyoming where precipitation can be bimodal but tends to occur in the form of rain. 

Region 5, with 471 HUCs, is located in New Mexico and is characterized by receiving a fair amount of 

monsoon precipitation and also has large temperature ranges. Region 6 is small, with only 73 HUCs, but 

is a climatically unique area of the SRLCC. This region, located in west central New Mexico, ranks the 

highest in both the isotherm and precipitation seasonality indices (fig. 5).

Region 1

Region 1 is the second largest climatic subregion of the SRLCC. It had many watersheds with a 

high index ranking located in the Uinta and Wasatch Mountain ranges of Utah, and the Uncompahgre 

Plateau and the Rocky Mountains of northern Colorado into Wyoming (Ironside, 2018).

Region 2

Region 2, the largest climatic subregion of the SRLCC, had many watersheds with a high index 

ranking. Many of the northern watersheds placed in the climatic subregion of the SRLCC ranked 

relatively high. Other watershed of high ranking occurred in southern Utah, in the Henry Mountains, 

Escalante area, and the Sevier Plateau. The San Juan Mountains of southwestern Colorado also had a 

high concentration of high-ranking watersheds (Ironside, 2018).

Region 3

The Laramie Mountains of Wyoming and the Front Range of Colorado parts of region 3 had the 

highest concentration of high-ranking watersheds. The Nacimiento Mountains of New Mexico also 

ranked high for prioritization of check-dam installation, along with parts of the Sangre de Cristo 

Mountains (Ironside, 2018).
12

Region 4

Region 4 had similar results to region 3, where watersheds ranked high in similar areas. Many of 

the lower elevation watersheds of the Laramie Mountains of Wyoming, the Front Range of Colorado, 

and the Nacimiento and Sangre de Cristo mountain ranges of New Mexico were found to rank high. For 

Arizona, some watersheds around the Sullivan Buttes and Mohon Mountain area, northwest of Prescott, 

ranked high (Ironside, 2018).

Region 5

The region 5 subregion is located in New Mexico and had watersheds ranking high east of the 

Santa Fe area and Sangre de Cristo Mountains. Three watersheds also ranked high in the Vera Cruz 

Mountains (Ironside, 2018).

Region 6

Region 6 is a small subregion. It had two watersheds that ranked highest—the Jaralosa Creek 

and the Dry Lake Canyon watersheds located in the Gallina Mountains (Ironside, 2018).

Discussion

Erosion has been a land-management concern in the region of the SRLCC over the past century. 

I provide a watershed ranking system across the SRLCC based on two of the most relatively static 

contributors to water erosion—Natural Resources Conservation Service (2017) soil type erodibility by 

water index (K) and slope gradient. Because of the landscape scale of this assessment and the long-term 

goals of using check-dams for future erosion control efforts, I did not include the more dynamic factors 

of precipitation intensity and cover that contribute to soil erosion rates. Precipitation intensity and cover 

(vegetative, physical soil crusting, biological soil crusts, crops) also play an important role in 

determining erosion rates (Bowker and others, 2008; Miller and others, 2011). These rather local and 

dynamic factors over time could also be considered by resource managers as the next step to prioritizing 

their local management areas for check-dam installation. Many other relatively dynamic factors over 

time can contribute to soil erosion rates, such as recent wildfire events, changes in weather patterns and 

extreme events (Sankey and others, 2017; Segura and others, 2014), and changing land-use such as 

grazing (Bryan, 1925), logging, mining, development, and agriculture. Information on the extent of 

existing beaver dams and existing check-dams across the SRLCC was not readily available for inclusion 

in my analysis; these are other factors that stakeholders may have local knowledge of to further 

prioritize watersheds within their jurisdiction.

Predicting actual stream sediment yields has proven difficult. Both the RUSLE (Renard and 

others, 1991) and the Geo-spatial interface of the Water Erosion Prediction Project (GeoWEPP; 

Renschler, 2003) model underestimated post-fire erosion rates when compared to actual measured 

erosion rates in Colorado (Miller and others, 2011). More research is needed to improve both relative 

and absolute accuracy of predicted erosion rates. Currently there are no simple, physically based 

methods for determining the appropriate critical source area and minimum source channel length values 

in response to variations in climate, soils, vegetation, and burn severity across the entire study area. 

More detailed studies of erosion-model parametrization sensitivity are needed to determine the relative 

importance of the different parameters in the plant-management files of predictive models such as 

GeoWEPP (Miller and others, 2011).
13

Conclusion

Several studies have shown the benefits of rigid, brush, and beaver dams on small streams and 

how they can also benefit downstream riparian systems. Because of the size of the SRLCC unit, a 

landscape-scale assessment was needed to begin prioritization of watersheds for stream restoration 

efforts using check-dams. This assessment, along with the existing literature that primarily covers 

forested landscapes, will be useful for identifying areas to target for stream restoration.

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ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20181127