quantifying geomorphic change at ephemeral stream restoration sites using a coupled model approach

Geomorphology 283 (2017) 1–16 Contents lists available at ScienceDirect Geomorphology journal homepage: www.elsevier.com/locate/geomorph Quantifying geomorphic change at ephemeral stream restoration sites using a coupled-model approach Laura M Norman a,⁎, Joel B Sankey b, David Dean b, Joshua Caster b, Stephen DeLong c, Whitney DeLong d, Jon D Pelletier d a U.S Geological Survey, Western Geographic Science Center, Tucson, AZ, USA U.S Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, Flagstaff, AZ, USA U.S Geological Survey, Earthquake Science Center, 345 Middlefield Rd MS 977, Menlo Park, CA, USA d University of Arizona, Department of Geosciences, Tucson, AZ, USA b c a r t i c l e i n f o Article history: Received 25 August 2016 Received in revised form 11 January 2017 Accepted 12 January 2017 Available online 21 January 2017 Keywords: Restoration Watershed models 2D flow models Terrestrial LiDAR a b s t r a c t Rock-detention structures are used as restoration treatments to engineer ephemeral stream channels of southeast Arizona, USA, to reduce streamflow velocity, limit erosion, retain sediment, and promote surface-water infiltration Structures are intended to aggrade incised stream channels, yet little quantified evidence of efficacy is available The goal of this 3-year study was to characterize the geomorphic impacts of rock-detention structures used as a restoration strategy and develop a methodology to predict the associated changes We studied reaches of two ephemeral streams with different watershed management histories: one where thousands of loose-rock check dams were installed 30 years prior to our study, and one with structures constructed at the beginning of our study The methods used included runoff, sediment transport, and geomorphic modelling and repeat terrestrial laser scanner (TLS) surveys to map landscape change Where discharge data were not available, event-based runoff was estimated using KINEROS2, a one-dimensional kinematic-wave runoff and erosion model Discharge measurements and estimates were used as input to a two-dimensional unsteady flow-and-sedimentation model (Nays2DH) that combined a gridded flow, transport, and bed and bank simulation with geomorphic change Through comparison of consecutive DEMs, the potential to substitute uncalibrated models to analyze stream restoration is introduced We demonstrate a new approach to assess hydraulics and associated patterns of aggradation and degradation resulting from the construction of check-dams and other transverse structures Notably, we find that stream restoration using rock-detention structures is effective across vastly different timescales Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons org/licenses/by-nc-nd/4.0/) Introduction Ephemeral stream channels in dryland ecosystems are especially vulnerable to land use change, climate change, drought, and variability in rainfall intensity Many studies have described how both natural and anthropogenic disturbances have altered dryland channel morphologies, including widespread channel incision, lateral erosion and increased flood magnitude over approximately the last 150 years (Lane, 1955; Schumm and Parker, 1973; Brady et al., 2001; Simon and Rinaldi, 2006; Norman, 2007; Norman et al., 2008a, 2008b; DeLong et al., 2012; Leopold et al., 2012; Dean and Schmidt, 2013; Villarreal et al., 2014; Norman et al., 2016; Dean et al., 2016; Norman and Niraula, 2016) Despite attempts to manage and restore dryland streams, incision and erosion continue (Karr and Chu, 1999) The lack of a generally accepted methodology for monitoring and evaluation of modified ⁎ Corresponding author E-mail address: lnorman@usgs.gov (L.M Norman) channels and misunderstanding of what techniques and not succeed at meeting local objectives contribute to project failures (Wohl et al., 2005; Tompkins and Kondolf, 2007; Palmer, 2009) The construction and installation of rock-detention structures has long been used to rehabilitate eroded watersheds, support flood control, and enhance water storage (DeBano and Schmidt, 1990) Rock-detention structures range in size from dams constructed like a spreader and stacked only one rock high (one-rock dam), to loose-rock check dams (or gully plugs), and larger rock-filled wire baskets (gabions) All of these serve the purpose to detain the flow of water and capture sediment (Fig 1) Boix-Fayos et al (2007) and Castillo et al (2007) investigated the effectiveness of check dams on the morphology of ephemeral channels in a semiarid, highly degraded catchment in Spain Upstream, check dams retained sediment and decreased the longitudinal gradient (Boix-Fayos et al., 2007; Castillo et al., 2007) Downstream changes in the cross-sectional shape of the stream channel, the composition of channel-bed material, and bankfull-depth measurements all indicated that the check dams caused erosion (Boix-Fayos et http://dx.doi.org/10.1016/j.geomorph.2017.01.017 0169-555X/Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 2 L.M Norman et al / Geomorphology 283 (2017) 1–16 Fig Sketches of rock-detention structures, including: (A) spreader (or one-rock dam), (B) loose-rock check dams (or gully plugs), and (C) larger rock-filled wire baskets (gabions) by Chloé Fandel al., 2007; Castillo et al., 2007) The amount of sediment stored by the check dams was higher than the amount of eroded material in the downstream reaches of the check dam Lenzi (2002) found that check dams in the north Italian Alps stabilized stream beds Many channel restoration projects are constructed without the aid of exploratory numerical modelling or formal engineering Moreover, the complex interactions between water and sediment that determine river morphology are difficult to predict, especially when perturbations are introduced This research aims to build a conceptual model of restoration design, characterize impacts of installing rock-detention structures (e.g., check dams and gabions) on the geomorphology of ephemeral stream channels, and demonstrate tools useful for restoration specialists We used output from a one-dimensional watershed model to develop and constrain boundary conditions for a two-dimensional computational model Ultra-high-resolution repeat topographic surveys were processed to develop time-sensitive representations of the terrain as a continuous surface for input and to validate model outputs An objective of our study was to demonstrate how the combination of high-resolution topographic change detection and streamflow modelling can be used to quantify where and how restoration with rock check dams can influence longitudinal and lateral streamflow and sediment deposition and to induce changes in the stream channel and bed of ephemeral stream restoration projects We hypothesized that new structures will incur sediment deposition and water depth increase upstream and that model results would identify potential locations of high-energy flows, scour, and deposition over time At historically modified sites, we hypothesized that modelling and topographic change detection would portray water conveyed through the reach and past the check dams with relatively minimal changes to the channel and bed topography Study area The Madrean Archipelago Ecoregion, in the southwestern United States and northwestern Mexico, is part of the Basin and Range Province, which is characterized by isolated forested mountain ranges (Sky Islands) and broad valleys or basins of deserts and grasslands The aridity, high-precipitation intensity during the thunderstorm season, and large topographic relief contribute to the high erodibility of this landscape (Parsons, 1995; Dickinson, 2010) Large-scale land use practices of the last century or two, including continuous cattle-grazing and reduction in wildfires by man, have further exacerbated erosional processes of this landscape that has led to the incision and lateral erosion of many of these channels (Baker et al., 1995) In the San Francisco Valley, New Mexico, a series of check dams from as early as CE 750 were found to reduce high flows, erosion, and gullying upstream (Anyon and LeBlanc, 1984; Doolittle, 1985) The Civilian Conservation Corps initiated multiple projects to install newer structures in the southwest USA, circa 1930s; but the impact on channel morphology is not well quantified locally (Gellis et al., 1995; Nichols et al., 2016) A bi-national community-based collaboration is newly formed of restoration practitioners, land managers, and scientists called the Sky Island Restoration Cooperative (SIRC) that is working to manage the landscapes of the Madrean Archipelago for future generations In 2015 and 2016, SIRC documented combined expenditures exceeding $4 million USD spent to preserve and promote ecological diversity, build resiliency in landscapes, and counteract erosion using rock-detention structures (Adams, 2016; Sky Island Restoration Cooperative, 2016) The SIRC has identified the need for investigating and quantifying impacts of management practices, for acquiring new tools and decision-support systems, and for integrating scientific investigations with previous knowledge to strengthen their investments In this 3-year study, we examined modified reaches of two ephemeral streams in southeast Arizona, USA, with different SIRC management histories (Fig 2) 2.1 Bone Creek (BC) The Bone Creek subwatershed (~ 46.5 ha) drains into the Stevens Canyon tributary of the Sonoita Creek, northeast of Patagonia, AZ (Fig 2; 31.57078, − 110, 74,124 at the confluence) It was incised during the 1983 El Nino when a cattle tank upstream was breached (Kate Tiron, Deep Dirt Farm Institute, Personal communication) Mean annual precipitation is 50.29 cm and average annual temperature is 16.43 °C (U.S Geological Survey, 2016) No stream gages are located in this canyon to measure discharge In 2014, land managers installed rock-detention structures to reduce active gullying and erosion The area of interest L.M Norman et al / Geomorphology 283 (2017) 1–16 Fig Map of SE Arizona displaying the location of the two study areas (AOI) selected in this study is 2213 m2, with ~103 m of the thalweg upstream and 76 m downstream of the main gabion structure (Fig 3) Range of elevation in the AOI varies between ~ 1263.5 and 1258.8 m (slope = 4.7/179; 2.6% grade) This gabion spanned most of the bankfull channel at installation The one-rock dam, which covered a portion of the channel bed, was located ~70 m upstream of the gabion The vegetation in the area is Mesquite (Prosopis velutina) tree savanna, with an understory of mixed-native perennial upland grasses including Bouteloua sp and Sporobolus sp The channel is dominated by perennial grasses with scattered perennial forbs and shrubs Nonnative species are present but not dominant; they include Eragrostis lehmanniana on the banks and Sorghum halepense in the channel Ground cover is variable, impacting sediment mobility Soils within the area are sandy and moderately cohesive 2.2 Turkey Pen (TP) The Turkey Pen (TP) subwatershed (769 ha) of Turkey Creek (Fig 2), on the southwest slopes of the Chiricahua Mountains, AZ, has been altered by N 2000 loose-rock structures installed or maintained since 1983 and is regularly grazed by cattle Turkey Creek drains steep slopes underlain by thin soils, regolith, and volcanic bedrock (DuBray and Pallister 1991) When restoration began, the channel was incised as a result of high-energy flow events during summer thunderstorm seasons (Anna Valer Clark, Cuenca los Ojos, Personal communication) Norman et al (2016) used a modified continuous slope area (CSA) gage in the TP watershed over the summer of 2013 at its outlet (31.87041–109.37284; Fig 4; cross sections 25–26) and documented hydrologic impacts of these structures that reduced runoff response (peak flow) yet discharge increased by 28% in volume over time Norman and Niraula (2016) used the Soil and Water Assessment Tool (SWAT; Arnold et al., 1998) to predict sediment yield in the subwatershed (∼ 356–483 ton/year) and suggested that check dams could retain ∼ 178–242 ton/year given mean daily discharge ~0.077 ms−1 The AOI in this modified study area is 1402 m2, contains six loose-rock check dams spaced ~20 m apart, extends along ~107 m of the thalweg, and terminates at the downstream-most structure (Fig 4) Range of elevation in the AOI varies between ~ 1730 and 1738 m (slope = 8/107; 7.5% grade) Vegetation is composed of mainly single-stem and bunchgrass (annuals) and outside of the channel, L.M Norman et al / Geomorphology 283 (2017) 1–16 Fig Photographs at Bone Creek looking downstream at (A) where the gabion was to be installed in 2013 and (B) the gabion after installation in 2015 (C) Map portraying cross section numbers and circles where check dams are located The gabion is the downstream circle oak-juniper-pinon woodland trees and boulders are found Average annual precipitation is 63.25 cm, and mean annual temperature is 12.18 °C (U.S Geological Survey, 2016) Methodology In order to simulate and compare the movement of water through the rock-detention structures and consider resulting evolution of topographic features, we applied a combination of field observations and numerical modelling High-resolution repeat topographic surveys were done in 2013 and again in 2015/2016, and change detection analysis was conducted for both study sites Discharge measurements collected during the summer of 2013 were used to represent peak flow at TP (Norman et al., 2016); however, at the ungaged BC, hydrologic simulations were necessary to derive estimates of discharge A 1D kinematic equation was applied to simulate runoff response of the watershed using the KINematic runoff and EROSion model (KINEROS2; Woolhiser et al., 1990; Semmens et al., 2008; Goodrich et al., 2012) KINEROS2 can simulate routing flow and sediment transport over long reaches but does not simulate lateral flows or the evolution of bed topography A 2D model can simulate complex flow fields generated by rock-detention structures and variations in channel shape such as high sinuosity and lateral dispersal of flow that cannot be represented by 1D models The discharge estimates derived from the KINEROS2 model and LiDAR-derived topography were used as input to the International River Interface Cooperative Nays2DH model to reproduce and examine changes in the spatial and temporal evolution of flow and sediment storage around rock-detention structures in the study reaches 3.1 Light Detection and Ranging (LiDAR) Terrestrial laser scanning (TLS or ground-based LiDAR) uses highspeed laser measurements to produce accurate three-dimensional (3D) point clouds that can then be processed to produce digital elevation models with high spatial resolution These can illuminate finescale topographic variability at the centimeter scale and when performed repeatedly can be used to map landscape change at centimeter-scale accuracy When coupled with other types of environmental data, change detection from multi temporal LiDAR data is a powerful tool for understanding geomorphic processes (Cavalli et al., 2008; DeLong et al., 2011, 2012; Collins et al., 2014, 2016; Goodwin et al., 2016) Results of high-resolution change detection methods provide a representation of areal and volumetric change that can be used to document surficial processes These methods quantify spatial and temporal differences in sediment flux in a variety of geomorphic contexts, including ephemeral streams and gullies (DeLong and Henderson, 2012; Collins et al., 2014) 3.1.1 Topographic survey and digital elevation model development The two study sites were surveyed at high resolution using a Terrestrial Laser Scanner (TLS) at the beginning of the study period and again 2–3 years later: January 2013, May 2015, and September 2015 at the BC study site and in March 2013 and March 2016 at the TP study site The TLS data were collected using a Leica© Scan Station C10 for both 2013 surveys and a Reigl© VZ1000 scanner for all 2015 and 2016 surveys Mean point density for these surveys within the AOI at each site and survey was ≫ 10.000 points/m2 (~ cm mean point separation L.M Norman et al / Geomorphology 283 (2017) 1–16 Fig Photographs at Turkey Pen looking upstream from the downstream end of the AOI (cross section 26) in (A) 2014 and (B) 2016 (C) Map portraying cross-section numbers and circles where check dams are located distance) The Leica scans were registered to global coordinates using a Leica Viva differential GNSS system, and the Riegl scans were registered to global coordinates using control points collected by a TopCon realtime kinematic (RTK) global positioning system (GPS) unit A 5-cm resolution DEM was developed for all TLS survey data using Maptek's I-Site Studio Prior to DEM development, the TLS data were processed by first filtering out vegetation and then registering the remaining ground surface data with Maptek's proprietary ‘smart sampling global registration’ For BC, the 2015 TLS surveys were registered to the January 2013 TLS survey The reported registration errors (root mean squared error, RMSE) were 0.022 and 0.023 m for the May 2015 and September 2015 TLS surveys, respectively For TP, the March 2013 TLS survey was registered to the March 2016 TLS survey with an RMSE of 0.022 m 3.1.2 Geomorphic change detection and threshold DEMs-of-difference Change detection analysis was conducted for both study sites using the registered 5-cm resolution DEMs Analysis followed the methods presented by Wheaton et al (2010, 2013, Geomorphic Change Detection software: http://gcd.joewheaton.org) where elevation uncertainty was modeled on a pixel-by-pixel level using a fuzzy inference system (FIS) With this approach, the propagated sums of modeled FIS errors for both DEMs were used to estimate confidence in the DEMof-difference (DoD) The FIS error models for both field sites incorporated surface slope and roughness, which are topographic characteristics that significantly influence elevation uncertainty (Wheaton et al 2010, 2013; Bangen et al 2016), as well as point density, which affects DEM surface geometry Change detection analysis with the FIS error model was conducted at a 95% confidence threshold (for description of probabilistic thresholding see Lane et al 2003) Additional filtering of threshold DoD results was done by masking out change within areas of dense vegetation Dense vegetation was defined as areas with a vegetation point density (classified during processing; i.e., bare earth points excluded) greater than one standard deviation above the mean vegetation point density at each study site Apparent changes in areas with a vegetation point density N3710 points/m2 for TP, and N1670 points/m2 for BC were masked out as potentially biologic rather than geomorphic change Based on these methods, we consider thresholded change detection results presented here to be conservative and to represent significant geomorphic change outside of intersurvey L.M Norman et al / Geomorphology 283 (2017) 1–16 vegetation differences We also show the results of the geomorphic change detection prior to applying the probabilistic thresholding (henceforth referred to as raw or unthresholded change detection) 3.2 Numerical modelling We coupled open-source models to develop and analyze the relationship between hydrologic response, management, and geomorphometrics 3.2.1 Kinematic runoff and erosion model The KINematic runoff and EROSion Model (KINEROS2) model was applied to the BC in order to determine discharge KINEROS2 is an event-oriented, spatially distributed, and physically based hydrologic simulation model developed at the USDA Agricultural Research Service (ARS) to estimate runoff, erosion, and sediment transport (Woolhiser et al 1990; Semmens et al 2008; Goodrich et al 2012) KINEROS2 has been used to determine response to land-cover change (Hernandez et al 2000), fire (Guertin et al 2005), and flood vulnerability (Norman et al 2010a, 2010b) Because data had not been collected at BC to calibrate the model, it was implemented via the Automated Geospatial Watershed Assessment (AGWA) tool to derive input parameters (Miller et al 2007; Kepner et al 2009; Goodrich et al 2012) The USDA has been collecting and reporting semiarid rainfall and runoff response to develop and validate simulation models (i.e., KINEROS2 and AGWA) at the nearby Walnut Gulch Experimental Watershed (WGEW) at Tombstone, AZ, since 1953 (Renard et al 2008) These data were used to validate the KINEROS2 model used in this study and also to provide comparable response rates in the BC The watershed was delineated and discretized using digital elevation model information and intersected with geospatial soils and land use data in AGWA KINEROS2 was used to simulate a 2-year, 1-h uniform precipitation event (3.43 cm) to create estimates of runoff and peak flow response (Norman et al 2013) 3.2.2 International River Interface Cooperative, Nays2DH Two-dimensional models allow for estimates about the inundation extent of the water surface, stream depth (stage), stream velocity, and shear stress throughout a modeled channel (Logan et al 2010) The International River Interface Cooperative (iRIC) public-domain modelling interface (iRIC Project 2016) provides a broad spectrum of 2D rivermodelling techniques that can be used for river-restoration design Nays2DH is a two-dimensional, depth-averaged, unsteady, coupled flow and sediment transport solver within the iRIC framework (Nelson et al 2010, 2015; iRIC Project 2016) Nays2DH is a combination of two models, Nays2D and Morpho2D, which use the numerical solution of the shallow water equations in a curvilinear orthogonal, structured grid (iRIC Project 2016) Nays2D is a 2D solver for calculating flow, sediment transport, bed evolution, and bank erosion in rivers (Shimizu 2002) Morpho2D is a model used to simulate the 2D morpho-dynamical changes in rivers (Takebayashi 2005; Takebayashi and Okabe 2009) Nays2DH can calculate two-dimensional river flow and bedform shifts using the standard iRIC river confluence model, bank erosion model, bedload-suspended load simulations in mixture sediment, bedload layer model and fixed bed model, and sediment supply rate from the upstream end (iRIC Project 2016) It can simulate hydraulic processes like backwatering and development of recirculation zones and eddies and can also simulate lateral differences in water-surface elevation or potential changes to channel alignment, like restoration structures that redirect flow explicitly in model topography (Nagata et al 2013; Ku et al 2015; Yamaguchi and Funaki 2015) In iRIC, a general curvilinear coordinate system grid is used to cover the AOI For our channel simulations, we used a numerical grid as follows: nI = 25 nodes in the streamwise (longitudinal) direction at TP and nI = 41 at BC and in the cross-stream (stream-normal) direction, nJ = 10 nodes (total cross-stream grid width = 10 m) for both reaches Model inputs included: • Bed topography, automated from the TLS survey data First we reduced the spatial extent of the 5-cm DEM (from TLS) to the channel and resampled to m For the 2013 TLS scans at BC, we added the gabion by increasing the Z value by 0.5 m (Z + 0.5 m) and added 0.32 m to represent the one-rock dam (Z + 0.32 m) Elevation was then imported, as a vector, to the model and mapped to nodes using a triangular-irregular network (TIN) • Peak flow rates were determined using the KINEROS2 model at BC and the modified gage at TP (Norman et al 2016) Various parameters were adjusted to simulate a one-hour storm event, with defaults being held for all other parameters We enabled bed deformation and specified uniform flow for water surface at downstream with continuous discharge • Manning's n values, assigned to each reach to describe the channel roughness, take into consideration the conditions and adjustments for vegetation that might impact the amount of energy lost through friction and that governs turbulence (Dalrymple and Benson 1967; Aldridge and Garrett 1973; Phillips and Tadayon 2006) The default value (0.030) is not appropriate for the reaches in our study but instead vary for stable channels comprised of cobble (0.03–0.05) to boulders (0.04–0.07) We assigned n = 0.045 at BC and added adjustments consisting of obstructions (check dams) to the base n value at TP, where n = 0.065 Various adjustments are available to set boundary conditions from limited observation data We applied a standard solver and enabled bed deformation Periodic boundary condition was disabled to calculate water surface downstream using uniform flow, a common iterative for modelling exercises The slope, velocity (upstream), and initial water surface were all set for uniform flow, which was calculated from geographic data The diameter of uniform bed material was set to 0.55 mm (default) The cubic-interpolated pseudoparticle (CIP) Method was used for the finite differencing applied to the advection terms in the momentum equations (Yabe and Ishikawa, 1990) For a more detailed description of the analytical solver for calculation of unsteady 2D plane flow and riverbed deformation using boundary-fitted coordinates within general curvilinear coordinates, visit the Nays2DH Solvers Manual (iRIC Project 2016) Results 4.1 Stream flow At BC, flow rates used for the hydraulic simulations are obtained from the KINEROS2 model, given a recurrence period of a 2-year, 1hour precipitation event The estimated peak flow (2.6 ms−1) was fed into the Nays2DH simulation as uniform flow for a 1-hour flow event using topography from the 2013 TLS scans (BC 2013) modified with rock structures The maximum depth resulting from Nays2DH was at cross section8, just upstream from the one-rock dam simulation (~0.77 m; Fig 5A) Velocity is portrayed as arrows pointing in the direction of flow when velocities are N1.5 ms−1 (Figs 5, 6) Average velocity (magnitude) predicted for this event is ~ 0.52 ms−1 (Table 1) Higher velocity flows are identified around the simulated gabion (cross section25) in BC 2013, on the east and on the west sides—indicating the additional energy and potential to breach the simulated gabion in these locations (Fig 5A) Using the same parameters but the September 2015 topography (BC 2015), the model generated a maximum depth of ~ 0.9 m at cross section 14 (Fig 5B), downstream from where the one-rock dam had been established, and average simulated velocity was ~0.51 ms−1 (Table 1) In TP, the highest flow magnitude recorded over the monsoon of 2013 at the outlet was 1.34 ms−1 in response to a 2-year, 6-hour precipitation event (Norman et al 2016) Using this peak discharge as continuous uniform flow with topography from the 2013 TLS scans (TP 2013), L.M Norman et al / Geomorphology 283 (2017) 1–16 Fig Bone Creek maps depict depth of water (color range) and velocity, when N1.5 m/s (arrows point in the direction of flow) of Nays2DH output in LiDAR surveys of (A) BC 2013 with simulated gabion and rock structures and (B) BC2015 Location of rock-detention structures are shown by black circles (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) the Nays2DH model predicts average flow depth of 0.138 m with maximum depth of ~0.48 m at the outlet of the study area (cross section26; Fig 6A) and average velocity is ~0.5 ms−1 (Table 1) The model run on topography from the 2016 TLS scans (TP 2016) simulates a slightly deeper average flow depth of 0.152 m, with maximum depth of ~0.48 m but now upstream at cross section (Fig 6B) and average velocity as TP 2013 (~0.5 ms−1; Table 1) Note that the maximum depth and velocity at TP were identical between the 2013 and 2016 TLS scans LiDAR change detection results using the 95% threshold at BC demonstrate that erosion occurred on the inner side of the bend upstream of the gabion and along the margins of the channel downstream of the gabion and deposition occurred upstream and immediately downstream of the gabion (Fig 7C) At TP, LiDAR change detection results using the 95% threshold depict show erosion occurring along the margins of the channel and near the third-most upstream check dam and deposition, occurring upstream of that check dam and at the downstream end of the TP reach (Fig 8C) 4.2 Sediment and channel morphologic change 4.2.1 DEM differencing Table shows the overall erosion and deposition in both study areas using the raw results, as well as 50% and 95% thresholding change detection scenarios The raw changes are presented to visualize spatially continuous patterns of change, variability, and uncertainty for the geomorphic change detection On a ratio scale, differences between the scenarios are minimal; however, change magnitude does show substantial differences The raw and 95% threshold results are different from the 50% threshold scenario on the order of two magnitudes, suggesting that these results likely make up the upper and lower extremes of change for interpretation (Table 2) During the ~3-year time frame for all change detection scenarios, the percent of total area with change in TP appears to have predominantly eroded in contrast to BC, which predominantly aggraded Average vertical changes portray both sites eroding comparably (~0.1 m), but with much more deposition vertically in BC (Table 2) 4.2.2 Modelling At BC, the Nays2DH model (hereafter the model) predicts changes in elevation that when visually compared to the raw unthresholded change detection results indicate deposition within the thalweg immediately upstream and downstream from the gabion (cross section 25; Fig 7) and erosion downstream of the gabion near the elbow and the terminus of the AOI (Figs 7A, B) The deposition predicted by the model in the thalweg between structures (cross sections 11–15) is also shown in the raw and in the thresholded change detection results (Fig 7) Erosion predicted along the cutbank in the far eastern portions at cross-sections 15–20 occurs in the raw change detection results (Fig 7B) but not in the thresholded change detection due to poor reconstruction of steep, undercut banks by the DEM (Fig 7C) Erosion predicted on the opposite cutbank at the gabion structure itself (west side of structure) is evident in the raw and, to a lesser degree, the thresholded change detection (cross sections 25–30; Fig 7) 8 L.M Norman et al / Geomorphology 283 (2017) 1–16 Fig Turkey Pen maps depict depth of water (color range) and velocity, when N1.0 m/s (arrows point in the direction of flow) of Nays2DH output in LiDAR surveys of (A) TP 2013 and (B) TP 2016 Location of rock-detention structures are shown by black circles (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) L.M Norman et al / Geomorphology 283 (2017) 1–16 Table Results of running the same flow model on DEMs acquired over time (Q = 2.6 ms−1 in BC; Q = 1.34 ms−1 in TP) Max depth (m) Avg depth (m) Standard deviation Standard error Avg velocity (ms−1) Standard deviation Standard error Avg, Froude number (Fr) Fr Max Fr Min BC 2013 BC 2015 TP 2013 TP 2016 0.77 0.19 0.21 0.01 0.52 0.60 0.03 0.33 1.40 0.90 0.19 0.22 0.01 0.51 0.58 0.03 0.32 1.48 0.48 0.14 0.12 0.01 0.50 0.43 0.03 0.47 1.76 0.50 0.15 0.12 0.01 0.50 0.39 0.02 0.39 1.09 At TP, the model predicts some of the spatial patterns of erosion and deposition shown by the raw change detection (Fig 8) At the second structure downstream (cross Section 5), flow is diverted to the right of the structure (Fig 6B) and erosion is predicted to occur at the southeast margin of the channel and deposition downstream of the third check dam (cross section 10; Fig 8A) In contrast, the raw change detection results predominantly show erosion through cross section 10 though this erosion is not shown in the thresholded change detection and is assumed to be due to low point density and dense vegetation in this portion of the survey area (Figs 8B, C) The flow trajectory continues to follow the right bank around the rock structure at the third structure downstream At the fourth check dam downstream, the flow model is visually similar to the raw and thresholded change detection results predicting erosion upstream and along the left bank near cross section 14 (vegetation density and pooled water below the structure excluded these results from the thresholded DoD; Fig 8) Deposition upstream and downstream of the fifth check dam (cross section 18) predicted by the model is partially supported by the raw change detection results (Figs 8A, B) There is a slight convergence of flow upstream of the fifth structure downstream, but some of the flow vectors once again show flow around the structure (Fig 6B), with erosion predicted to occur at the channel margins and a small amount of deposition predicted to occur immediately upstream (Fig 8) Toward the AOI terminus (sixth structure; cross section 26), the model prediction differed with the raw and thresholded change detection results where a mix of erosion and deposition were captured by the change detection (Fig 8) The sixth check dam is situated at a road crossing and above a culvert (not included in the model), that is likely altering the natural patterns there 4.3 Comparison At BC, the shapes of the 2013 and 2015 thalweg long profiles for thresholded bed elevation and modeled water surface elevations are quite different (Fig 9) There is great variation in the LiDAR-derived bed elevation change at cross sections between 2013 and 2015 (ranging from −0.12 to +0.25 m), especially downstream from structures The thalweg long profiles for bed elevation and the bed elevation changes suggest that overall there was more bed aggradation from sediment deposition upstream of the gabion and more erosion downstream (Fig 9) The structures and subsequent changes in bed elevation over time probably impact velocity throughout the channel Velocity increases just before the gabion as the cross-sectional area gets smaller (constricts) and the flow must accelerate (Fig 9) The model depicts an increase in minimum WSE upstream of the one-rock structure and the gabion, where pooling effects could be due to detention, and the model depicts a decrease in minimum WSE downstream of the gabion over time (Fig 9) At TP, the shapes of the downstream long profiles for the thresholded 2013 and 2016 bed elevation and modeled WSE are relatively similar for much of the reach (Fig 10) The LiDAR-derived thresholded bed elevation change does not depict changes at cross sections that are as great as at BC (ranging from −0.18 to +0.1), with the biggest scour occurring upstream and downstream of check dam #3 Some deposition occurred at cross-sections downstream of check dam #2 and upstream of check dam #5 There is a noticeable decrease in the thalweg bed elevation in 2015 between check dams #1 and #2 that may represent interpolation errors caused by low TLS point density and inconsistent spatial geometry between the surveys in this portion of the survey area The model depicts some decreases in velocity near check dams #2 and #3 and increases downstream near check dams #4 and #5 The model depicts a particularly noticeable decrease in minimum WSE upstream of check dam #2 Discussion Bone Creek is an incised reach where the new gabion is tall relative to the width of the channel At BC, we hypothesized that water pools and sediment deposition would occur upstream of the new gabion and that our coupled model approach could identify locations of highenergy flows, scour, and deposition The model shows that deposition is likely to occur in the center of the channel at the upstream ‘onerock dam’, thereby spreading flow outward, promoting higher shear stress and causing the channel to widen at the structure Downstream from that structure, the hydraulics driven by the curvature of the channel results in erosion of the outer banks, and deposition on the inner banks, simulated in the model and corroborated with the LiDAR surveys This promotes channel migration, incorporation of bank sediment into the channel for infilling and aggrading, lowering of channel slope, and dissipation of energy The long profile of the bed and WSE depicts where shear stress is highest and may add to where erosion and deposition might be more likely to occur longitudinally throughout the reach (Fig 9) The model predicts increased flow depth and a backwater effect upstream from the gabion and a decreased overall velocity through time Modelling also demonstrates deeper flows immediately downstream of the gabion as well as along the bend between the upstream rock structure and the gabion, coincident with predictions of sediment deposition that were confirmed by the LiDAR change detection The Table Table depicts geomorphic change detection raw results, as well as 50% and 95% thresholding scenarios BC: 30 months TP: 36 months Change detection scenario Raw change 50% threshold change 95% threshold change Raw change 50% threshold change 95% threshold change Erosion area (m2) Percent of total area eroded Deposition area (m2) Percent of total area deposition Erosion volume (m3) Deposition volume (m3) Average erosion vertical change (m) Average deposition vertical change (m) Average (SD) vertical change (m) Net vertical change (m) 1095.9 51.0% 1039.33 48.4% 115.54 95.94 0.11 0.09 0.10 −0.01 94.83 4.4% 147.91 6.9% 11.4 15.6% 0.12 0.00 0.05 −0.05 31.58 1.5% 75.32 3.5% 2.98 10.46 0.09 0.14 0.13 0.07 952.07 68.6% 412.72 29.7% 163.74 19.92 0.17 0.05 0.13 −0.11 267.53 19.3% 184.05 13.3% 29.41 9.09 0.11 0.05 0.09 −0.04 40.7 2.9% 21.72 1.6% 4.37 1.14 0.11 0.05 0.09 −0.05 10 L.M Norman et al / Geomorphology 283 (2017) 1–16 L.M Norman et al / Geomorphology 283 (2017) 1–16 owner of the Bone Creek property recalls that erosion has occurred in the past high along the channel bend from lateral widening during the driest months when soil moisture was very low (Kate Tirion, Deep Dirt Farm Institute, Personal communication); this might be one source of the deposited sediment within the channel that we detected If a goal of installing rock structures at an incised ephemeral stream like BC is to help the channel aggrade, the channel banks may be a source of sediment that can be transferred to the channel beds Though no particularly large areas of significant erosion were detected by the LiDAR change detection upstream of the gabion at BC, the flow modelling and raw LiDAR change detection suggest that the steep slopes identified by the landowner above the channel bend could be a potential source in addition to the primary sediment supplied by the upstream watershed Deposition within the channel could result in a desired restoration outcome of an aggraded but wider channel over time upstream of the gabion It would be necessary to maintain the gabion, however, and avoid breaches during future high flows The hydraulic model also proves useful for identifying where the gabion could be vulnerable to potential breaches For example, the flow direction lines clearly point to the sides of structures in Fig 5, which are also locations of either predicted or detected erosion in Fig Therefore, iterations of the model with future topography could provide valuable insight as to where high energy flows could scour channels, identify hotspots of erosion and potential sheer, and could be used to test alternative restoration scenarios Since our study, a new rock structure and a new gabion have been installed near the terminus of the AOI where erosion was predicted by the model, and vegetation has been planted to strengthen the structures Turkey Pen used to be a deeply incised channel that has had decades to recover following installation and maintenance of check dams These rock structures are not tall relative to the width of the channel (e.g., in comparison to the gabion at BC), and the channel bed upstream of each check dam has aggraded nearly to the elevation of the dam This produces a stepped downstream long profile for the bed and for the water surface elevation Adapted vegetation provides friction and roughness that further reduce energy (depicted in the model by increased Manning's n) In TP, it was hypothesized that modelling and topographic change detection would indicate that water is currently conveyed through the reach and past the check dams with relatively minimal changes to the channel and bed topography The model portrays rock structures forcing flow outward toward the margins of the channel, similar to the structures at BC However, given that there has already been a substantial amount of sediment deposited behind these structures and given the relatively short height of the structures relative to the width of the channel, flow appears to be more effectively diverted around the structures; and this also appears to be reflected in the distribution of flow vectors Some sediment was eroded from several locations within the reach and that much of this sediment was probably deposited near the terminus of the AOI Nonetheless, when compared to a newly modified reach like BC, the ephemeral stream at TP currently appears to convey water and sediment with relatively minor impacts to the channel and bed Some researchers have suggested a limited potential for the lifespan of rock-detention structures to impact sediment (Polyakov et al 2014; Nichols et al 2016).We argue that even 30 years after installation, the rock structures are functional and sustainable, despite the inevitable decline in sediment capture over time and the need for periodic maintenance cited by landowners and managers We attribute this to the initial efficiency of rock-detention structures to attenuate peak flows (Norman et al., 2010a, 2010b) and trap sediment, which can also aid in the increase of surface-water availability (Norman and Niraula, 2016; Norman et al., 11 2016) and in the support and maintenance of vegetation (Norman et al., 2014) More studies are needed, however, to sufficiently describe this interconnected relationship, quantify vegetation response, and consider the potential of rock-detention structures to capture and store not only sediment but elements and nutrients like carbon and nitrogen, for example 5.1 Calibration The model is event-based, and the simulation we present depicts a 1-h flow At TP, discharge data from the monsoon of 2013 (Norman et al 2015) were used as input to the 2D model At BC, there is no discharge data available and so an uncalibrated 1D model is employed to predict peak flows, which brings some uncertainty into the 2D model However, given that we are looking for trends and patterns of geomorphologic impacts from restoration structures given high flow events, we feel that getting the exact number for peak flow discharge is not limiting to this application and that a ‘ballpark’ estimate of high flows suffices to consider impacts in the 2D flow model These two different peak-flow discharge quantities (2.6 and 1.34) were fed into the Nays2DH model for 1-h, presuming uniform flow, to examine the impact of high flows on runoff, erosion, and deposition in the channel around rock-detention structures The model predicts elevation changes incurred after a simulated 1-h high flow event, which at the pixel and cross section scale would not be expected to correlate exactly with topographic changes detected over the course of ~3 years with LiDAR However, the model does appear to predict patterns of erosion and deposition that are supported by LiDAR surveys, and because of this we have relatively high confidence in the potential of the model to identify locations of high energy flows, potential scour, sheer, and erosion Future work could focus on iteratively calibrating the models, using observed changes from LiDAR as reference data, and manually adjusting variables to achieve the desired outputs First, it would be necessary to overcome the discrepancy between the relevant time periods simulated in the modelling and measured with the LiDAR, but it is plausible that this could produce more accurate model predictions of trends and magnitudes (especially if we were able to monitor an actual peak flow event at the study sites) and could document geomorphology, flow rates, depth, and sediment transport before and after the event However, comprehensive field data is often unavailable in small watersheds, and it is prohibitive to constrain analyses to where stream gages or longterm field campaigns might exist Furthermore, modelling errors can be introduced by overfitting individual parameters in order to produce a desired output (Vogel and Sankarasubramanian 2003) Some scientists contend that process-based models are less reliant on calibration and therefore less sensitive to this introduction of error (Goodrich et al 2012; Fatichi et al 2016) Fortunately, deterministic models can be applied in virtual laboratories and provide very useful approximations of reality When coupled with high-resolution repeat topography, we have the explanatory power to go back in time and evaluate relative changes in watershed response Given these results, we find that hydraulic code can be used in un-gaged reaches to predict relative change and denote qualitative differences in varied scenarios to better understand and quantify the impacts of rock-detention structures 5.2 Change detection In this study we present results of raw (unfiltered) and FIS 95% confidence threshold LiDAR change detection results (e.g., Figs 7B,C and 8B,C) The DoDs produced from the change detection have high Fig Maps depict BC erosion/deposition results from (A) the Nays2DH model iteration run on 2013 DEM with predicted elevation change overlain on the 2015 hillshade; (B) the raw unfiltered LiDAR 2013–2016 change detection results; and (C) the LiDAR 2013–2016 95% confidence threshold change detection results, where rock-detention structures are portrayed inside black circles All maps are overlain on the 2015 hillshade map and portrayed using graduated colors to show deposition gradients in blue, erosion gradients in red, and little (zero) change as white or not reported (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 12 L.M Norman et al / Geomorphology 283 (2017) 1–16 Fig Maps depict TP erosion/deposition results from (A) the Nays2DH model iteration run on the 2013 DEM with predicted elevation change; (B) the raw unfiltered LiDAR 2013–2016 change detection results; and (C) the LiDAR 2013–2016 95% confidence threshold change detection result, where rock-detention structures are portrayed inside black circles All maps are overlain on the 2015 hillshade map and portrayed using graduated colors to show deposition gradients in blue, erosion gradients in red, and little (zero) change as white or not reported (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) accuracy and are based on well-controlled pairs of repeat LiDAR surveys averaging 1-cm point spacing and interpolated to 5-cm raster cells depicting the bare earth topography We selected the FIS 95% confidence threshold as a commonly applied and conservative approach to identify statistically significant topographic changes (Lane, 2003; Wheaton et al 2010, 2013; Bangen et al 2016) Interestingly, the raw LiDAR data show very sizeable geomorphic changes that are filtered out by the threshold method For example, Fig shows that large areas of the reaches of BC upstream of the structure near cross section9 and downstream of the gabion near cross section25 had from 0.04 to 1.4 m of erosion of which little or no significant change appears on the 95% confidence threshold map The same is true for TP where a large area of ~0.4 to 2.9 m of the northern reach is omitted from the filtered map due to uncertainty based on low point densities in the upstream direction and on density of vegetation These examples of extensive, deep omissions are evidence of how useful, yet conservative, thresholding can be In the examples for BC upstream of cross section9 and for TP, omission was primarily the result of low TLS ground point density and/or high vegetation point density (areas of high elevation uncertainty) It is important to note that threshold-based change detection is an automated method to identify where we can confidently identify areas of change (area of low elevation uncertainty) and does not represent the entirety of geomorphic change For example, cutbanks between cross-sections 16 and 20 and 25 and 28 at BC had high elevation uncertainty attributed to steep, rough slopes that resulted in omission of likely changes in eroding channel banks and associated slump deposits L.M Norman et al / Geomorphology 283 (2017) 1–16 13 Fig Long profile at BC with changes in bed elevation, modeled water surface elevation, and velocity depicted We illustrated the importance of evaluating the raw and thresholded results, particularly for incised ephemeral stream restoration Most areas of deposition within the raw results were only between ~ 0.001 and 0.3 (at BC) or 0.001 and 0.4 (TP), though summing the average depth of deposition over large areas could represent an underestimation of change within thresholded data; and conversely, inclusion of extensive areas of change less than the survey spatial registration of 0.022 m and local ground vegetation canopy may overestimate change within the raw data For example, most of the channel between the upstream structure and downstream gabion at BC had spatially continuous deposition of b 0.28 m in the raw change detection that was filtered out by the 95% threshold approach due to low TLS ground point density and high vegetation point density The same is true at TP along the channel and the right-hand channel margin downstream of the third, fourth and fifth check dams James et al (2012) argued that large components of sediment budgets may be overlooked by such filtering, and there can also be systematic bias where, for example, thin overbank floodplain deposits are systematically filtered out; while deeper in-channel and channel bank erosion is retained We not believe the FIS 95% confidence threshold method is too stringent of a standard for geomorphic change detection in this particular environment but that it likely represents the minimum level of change For practical purposes it seems that the raw (or possibly a less conservative threshold approach) and thresholded change detection results provide a realistic and spatially descriptive depiction of the geomorphic response for ephemeral stream channels to restoration Fig 10 Long profile at TP with changes in bed elevation, modeled water surface elevation, and velocity depicted 14 L.M Norman et al / Geomorphology 283 (2017) 1–16 with rock-detention structures The raw change detection shows widespread and substantial changes, yet it would be easy to instead interpret the conservative thresholded output as representing a stable system with little sediment movement or channel morphologic change This could be particularly important, for example, if the LiDAR change detection and specifically the morphometric method, which uses geomorphic change detection (DoDs) to compute change in volume and to determine sediment budgets (Lane 2000; Lane et al 1995; Brasington et al 2003; Gaeuman et al 2003; Lane et al 2003; Martin and Ham 2005), were used to qualitatively or quantitatively calibrate runoff, erosion, and/or flow modelling The change in sediment storage during the observation period was + 7.48 m3 at BC and − 3.23 m3 at TP, suggesting that the systems aggraded and eroded, respectively, during the observation period These specific or relative changes could be used in further investigation 5.3 Conclusions In this study we compare the modeled impacts of peak-flow events to geomorphic changes observed over a 3-year timespan at two ephemeral streams following modification with rock-detention structures at different timescales At one stream, structures installed 30 years ago persist in stabilizing the channel due to the interstructure impacts on flow (sedimentation and, later, vegetation establishment) We show that this stream with long recovery time since modification has topographic and modeled flow characteristics that are in relative equilibrium with one and other In contrast, at the stream with a new gabion installed, the topography appeared to actively evolve in a manner consistent with our hypotheses and the modeled flow characteristics The model depicts changes in average velocity (reduced by 16%) and maximum depth of water (increased by 12%) over the 3year period, both of which allow for longer residence time and the potential for greater infiltration We suggest that not only are the structures creating these changes, but they are influencing sedimentation; and the variations in bed elevation and change in geomorphology stimulates these effects Our comparison of raw vs thresholded topographic data in this study shows that there is an important tradeoff to consider for informing restoration science The thresholded change detection results provide high confidence in changes of magnitude but, perhaps, are overly conservative The raw change detection results include more quantitative uncertainty but better detect patterns of topographic change We demonstrate that integrating very accurate, high-resolution topographic measurements made at the proper scale (with LiDAR in this study), with process models of streamflow and sediment transport, can provide restoration practitioners with useful information to be used in project design, maintenance, and improvement This coupled-model approach can be useful to plan restoration projects or make predictions about proposed restoration activities as the design and placement of rock-detention structures need to consider the topographic slope, expected peak flow, and expected erosion and sediment deposition Environmental practitioners and scientists alike can use this example to document restoration impacts and subsequent landscape response to secure funding and valuable partnerships for future projects Acknowledgements This research could not have been conducted without the restoration work done and access to land by Anna Valer Clark and Josiah Austin (El Coronado Ranch), Kate Tirion (Deep Dirt Farm Institute), and Drs H Ron Pulliam & David Seibert (Borderlands Restoration, LLC and The Biophilia Foundation) We want to thank our colleagues at the USDA-ARS, Drs Dave Goodrich & D Phillip Guertin, who advised us in our early acquisition and model endeavors, as well as our colleagues in the USGS who helped 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Dryland Arroyo Channels Lead to Resilient Riparian and Cienega Restoration? Observations from LiDAR, Monitoring and Modeling at Rancho San Bernardino, Sonora MX Presented at the AGU, San Francisco,... digital elevation model information and intersected with geospatial soils and land use data in AGWA KINEROS2 was used to simulate a 2-year, 1-h uniform precipitation event (3.43 cm) to create