SOUTHERN SIERRA CRITICAL ZONE OBSERVATORY - KREW PROJECT REQUEST FORM

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1 SOUTHERN SIERRA CRITICAL ZONE OBSERVATORY - KREW PROJECT REQUEST FORM Project Director/Principal Investigator (include contact information): Aniela Chamorro, PhD Student, Department of Geology & Geophysics, Texas A&M University Collaborators: John R. Giardino, Bradford Wilcox, Carol Chamorro Project Title: Soil Depth Model Project proposal (background, justification, objectives, methods, timeline, etc): 1. Background The Southern Sierra CZO has served as a field laboratory for numerous studies since 2009 (Bales et al., 2011; Jessup et al., 2011; Tague et al., 2009; Liu et al., 2012). In a comparative analysis of a paired-catchment study inside Providence catchment, Bales et al. (2011) demonstrated that the sub-catchment with deeper average soil depths resulted in values of annual streamflow that are 50 to 75% of the sub-catchment with thinner soils. Noting the uniformity of the physical and morphological properties of soils, Bales et al. (2011) suggest that depth of soil is one of the most important components used in hydrologic modeling. Findings of Bales et al. (2011) are in agreement and support previous work (Hopp & McDonnell, 1999; Fu et al., 2011; Nicotina et al., 2011, among others). The representation of the thickness of depth across the catchment is challenging, but important to obtain a more accurate prediction of water storage in soils and determine water flow pathways in the subsurface. Also, I argue that values of soil depth are not only important to obtain soil-water capacity of the catchment, but they also are important for the entire weathering zone. Using a linear regression method, Bales et al. (2011) represent the values of soil depth via a continuous grid using auguring measurements to a maximum of 1 meter depth. Because they did not reach bedrock in many of the boring holes, they point out, however, that soils are potentially deeper across the Providence catchment (Bales et al., 2011). Their field collection method used a sampling grid with 123 meter spacing. Later surveys performed by SSNCZO (personal communication with Peter Hartsough and Matthew Meadows, 2012) show, actually, that much deeper soils exist in the area. In some locations, soil depths extend to 7 meters. The existence of deeper soils could explain the poor results of the depth representation developed by Bales et al. (2011): their regression model accounts only for 16% of the variance in soil depth. Unfortunately, determining the profile of the weathering zone profile is time consuming and expensive. For that reason, correlation between quantitative and qualitative characterization of the surface and the profile of weathering is thought to be as a useful predictive relationship (McKenzie et al., 1999). Soil-landscape relationships are unique for each site with unique pedogenetic signatures (Dietrich et al., 1995). In the area of

2 study, new indexes of geomorphological and vegetation could be tested. Unfortunately, there are not effective predictors for variables such as bioturbation and geomorphological patterns (i.e. stepped topography) although its significance in controlling pedogenesis and soil distribution have been hypothesized (Dietrich et al., 1995; Heimsath et al., 1999; Butler et al., 1995; McKenzie et al., 1999). Understanding the relationship between depth of the zone of weathering and surface expression becomes even more complicated in areas with highly vegetated, inhomogeneous bedrock. Fortunately, the SSNCZO site, whereas it is highly vegetated, it is situated on homogeneous, granitic bedrock (Bateman, 1972). Research questions addressed in this work are: 1) Can variables influencing pedogenesis in other landscapes work for predictions of depth of the weathering zone in a watershed of the Southern Sierra Nevada? 2) Can elevation of vegetation and patterns of steeped topography work as good indicators of depths of the weathering zone? 3) Is the heterogeneity of the depth of the weathering zone across a catchment relevant to the hydraulic connectivity in the subsurface? 4) How does the relevance of the depth of the weathering zone in hydrology predictions vary across different scales? 2. Justification Although the Southern Sierra CZO group has been monitoring atmospheric and hydrologic effects on soils, the spatial representation of the heterogeneity of subsurface and their causes remain a challenge because of the technical difficulties of obtaining data. Mapping variations of zones of weathering, the thickness and also the developing of geomorphological maps can provide a framework to develop and improve the predictions of process-based models and to improve surveys and communication among the CZO community (Lin, 2010). Spatial information on the depth of soil is also relevant for contaminant transport problems, studying the chemistry of alpine water, carbon sequestration work (Hancock et al., 2010), and land-atmosphere exchanges occurring at the surface (Gochis et al., 2010). This dissertation work undertaken in the CZO has the potential to help with predictions in other Mediterranean mountain regions through the development of new methodologies and the analysis of data that are unavailable in other parts of the world. The spatial variability of water storage in soils can be essential for defining projects of land-use change, irrigation, construction of dams, and forest management.

3 3. Objectives Determine if there is a relationship between depth of weathering and geomorphic surface expression Determine and model the subsurface hydrological connectivity of the Providence watershed 4. Methods Objective 1. Determine if there is a relationship between depth of the weathering zone and the geomorphic surface expression To determine the correlation between the depths of the weathering zone and geomorphological indices, I will: i) Develop a continuous spatial representation of the zone of the weathering from auguring measurements Measurements of depth of soil will be provided by the SSNCZO. These measurements were made from 2010 to 2012, up to 6 meters in depth. All the field sampling was made following a 123 meters grid pattern designed by the SSNCZO group; the samples were collected using a stainless-steel hand auger. This method of sampling presents two limitations for the objectives of this study: 1) simple kriging performed has shown that the distance between sampling locations does not capture the heterogeneity of the variable, and 2) there is no certainty if each sample core reached bedrock in each of the auguring. To address this limitation, I propose to determine the depth to the top of bedrock using Ground Penetrating Radar, a geophysical method. Transects between interfluves will be collected at three different elevations in the Providence catchment. The goal of these transects is to build a raster-data structure combining auguring and electrical resistivity. Lateral, spatial variation not captured by the auguring measurements will be complemented by the continuous measurements of the geophysical technique. ii) Develop a model of the depth of the zone of weathering I will use geostatistics to build a quantitative model based on field-point measurements (i.e., auguring and electrical resistivity transects). The product will be a model that captures the lateral, spatial variability of depth of the weathering zone. To accomplish a continuum model that captures the heterogeneity of the depth across the catchment, geostatistics will be integrated with an environmental correlation approach. I will use a Geographic Information Systems (GIS) to relate and analyze the spatial data using ArcMap 10 and R software. The statistical analysis will be done in the statistical R environment, following Hengl et al. (2007) and Olea et al. (2009).

4 iii) Geomorphology Mapping A geomorphology map of Providence catchment will be constructed. To create the map, critical geomorphological data will be collected, including individual landforms, processes that operated or are currently operating in the area, geological information, historical land-use data, location of faults, and tectonic geomorphological descriptions of the Sierra Nevada Mountains (Wahrhaftig, 1965; House et al., 2001; Phillips, 2008, among others). As described by Verstappen (2009) these data should include roads and other anthropogenic disturbances. Despite the fact that the area of Providence Catchment is highly vegetated, LIDAR data collected in will allow extraction of vegetation cover, which will facilitate a qualitative analysis of terrain with a maximum resolution of 1 meter. In the field, the catchment will be surveyed to locate various landforms including rockfall areas, boulder creeks, the presence and location of terracettes, accumulation of slope material behind large trees and debris flows. From field work, morphological units or morphological regions will be mapped using the automated landscape classification in ArcMap 10 and R. Symbols used on the map will follow the style developed of Demek (1967) and Vertappen (1983) for geomorphic mapping. The validity of the geomorphic map will be tested using block regression kriging. To contrast the results of this geostatistical technique with a non-geostatistical assumption, moving window regression will also be performed. Error will be measured using a bootstrapping method. The same technique was used by Dahlke et al. (2009), who pointed out that the number of points for each geomorphological unit (homogeneous) could influence evaluation. Objective 2. Determine the subsurface hydrological connectivity within Providence watershed. Once the spatial patterns of the bedrock profile are determined through GPR analysis, I propose to study the hydrological link to the underground morphology of the watershed through an virtual experiment. The goal is to develop a conceptual model of the relationship between depth of soil and the subsequent hydrological response though a sensitivity analysis. HYDRUS 3D will be used. It requires using hydrological and topographic information. HYDRUS requires parameterization of the properties of soil and bedrock, and this information will be obtained from data available through the CZO program. The virtual experiment will be conducted to observe the influence of bedrock configuration on hydrological response, specifically streamflow. Compared with other models that account for hillslope connectivity (DHSVM, RHEESSYS, TOPOG, EPA s SWIMM) (Tague, 2005), HYDRUS 3D is the best, as it can model saturated and unsaturated media in 3D, while accounting for lateral fluxes in the regolith. Information about the variation of the vertical hydraulic conductivity is available for the first meter of soil when permeability is considered favorable for

5 subsurface flow (Bales et al., 2012). The depth of the soil model developed in the previous objective will be added to the calibrated system to test the correlation between the modeled and actual values of streamflow. The tested values for sensitivity will be depth of soil and hydraulic conductivity. Initial conditions, boundary conditions and time of simulation will be obtained from selected isolated storms with field-determined observations of flow. This step will require a detailed digital terrain analysis of both surface and bedrock, which will be created using the GPR survey of the hillslope under study. The dimensions of the study area on the hillslope will approximately 45 x 20 m. It is thought that bedrock heterogeneity will cause storage of water in saturated pockets interrupting the linear subsurface flow and creating a more complex, dynamic response of subsurface flow. This storage could affect timing of subsurface flow and subsequently the amount and timing of streamflow. The virtual experiment will be performed to observe this flow using real data. Although HYDRUS 3D cannot simulate overland flow, this is not expected to be a limitation because past research has indicated that overland flow is not the dominant hydrological process in the area(bales et al., 2011; Hunsaker et al., 2011; Liu et al., 2012). 5. Timeline The GPR surveys will be completed in 2 weeks, from 1 to 15 July of The completion of the dissertation will take approximately 9 months. I expect to graduate in May 2014.

6 Describe the project location (attach map if necessary): The project is located in Providence Catchment. The method is non-invasive and nondestructive. The locations where the GPR lines will be collected are shown in turquoise:

7 Duration and period of use: The surveys will be done from July 1 to July 15 in 2013 Describe any markers, including tags, flagging, stakes, fencing, or other to be used: There is no need for permanent markers in the field. During the collection of lines, small flags will be used and retired once the survey is completed in the same day. Will other USFS facilities or resources be needed? X Yes No If Yes, please describe: Sanitary installations, shower, electricity and camp site (in case that a barrack accommodation is not available). Do you intend to publish your results? X Yes No If Yes, please remember to site necessary parties (ex. SSCZO, KREW, etc.) If implementation of your project includes use of humans as experimental subjects or radiation, biological, or toxic chemicals, please explain. No apply Please provide the required proof of liability insurance, or fill out the necessary UC Merced liability waiver form before accessing research site. OK.

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