skip to main content


Title: Inverting Topography for Landscape Evolution Model Process Representation: 3. Determining Parameter Ranges for Select Mature Geomorphic Transport Laws and Connecting Changes in Fluvial Erodibility to Changes in Climate

We review select mature geomorphic transport laws for use in temperate ridge and valley landscapes and compile parameter estimates for use in applications. This work is motivated by a case study of sensitivity analysis, calibration, validation, multimodel comparison, and prediction under uncertainty, which required bounding values for parameter ranges. Considered geomorphic transport formulae span hillslope sediment transport, soil production, and erosion by surface water. We compile or derive estimates for the parameters in these transport formulae. Additionally, we address a common challenge—connecting changes in precipitation distribution to changes in effective erodibility—by using a simple hydrologic model and a method to estimate precipitation distribution parameters using commonly available data. While some parameters are reasonably well constrained, others span orders of magnitude. Some, such as soil infiltration capacity, have a direct physical meaning but are challenging to measure on geologically relevant timescales. Through the process of compiling these ranges we identify common challenges in parameter determination. The issue of comparable units derives from considering an exponent as an empirically inferred coefficient rather than as an expression of a fundamental relationship. The issue of appropriate timescales derives from the mismatch between human measurement and geologic timescales. This contribution thus serves both as a practical compilation for applications and as a synthesis of outstanding challenges in parameter selection for geomorphic transport laws.

 
more » « less
Award ID(s):
1725774 1831623
NSF-PAR ID:
10446797
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  ;  
Publisher / Repository:
DOI PREFIX: 10.1029
Date Published:
Journal Name:
Journal of Geophysical Research: Earth Surface
Volume:
125
Issue:
7
ISSN:
2169-9003
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Site description. This data package consists of data obtained from sampling surface soil (the 0-7.6 cm depth profile) in black mangrove (Avicennia germinans) dominated forest and black needlerush (Juncus roemerianus) saltmarsh along the Gulf of Mexico coastline in peninsular west-central Florida, USA. This location has a subtropical climate with mean daily temperatures ranging from 15.4 °C in January to 27.8 °C in August, and annual precipitation of 1336 mm. Precipitation falls as rain primarily between June and September. Tides are semi-diurnal, with 0.57 m median amplitudes during the year preceding sampling (U.S. NOAA National Ocean Service, Clearwater Beach, Florida, station 8726724). Sea-level rise is 4.0 ± 0.6 mm per year (1973-2020 trend, mean ± 95 % confidence interval, NOAA NOS Clearwater Beach station). The A. germinans mangrove zone is either adjacent to water or fringed on the seaward side by a narrow band of red mangrove (Rhizophora mangle). A near-monoculture of J. roemerianus is often adjacent to and immediately landward of the A. germinans zone. The transition from the mangrove to the J. roemerianus zone is variable in our study area. An abrupt edge between closed-canopy mangrove and J. roemerianus monoculture may extend for up to several hundred meters in some locations, while other stretches of ecotone present a gradual transition where smaller, widely spaced trees are interspersed into the herbaceous marsh. Juncus roemerianus then extends landward to a high marsh patchwork of succulent halophytes (including Salicornia bigellovi, Sesuvium sp., and Batis maritima), scattered dwarf mangrove, and salt pans, followed in turn by upland vegetation that includes Pinus sp. and Serenoa repens. Field design and sample collection. We established three study sites spaced at approximately 5 km intervals along the western coastline of the central Florida peninsula. The sites consisted of the Salt Springs (28.3298°, -82.7274°), Energy Marine Center (28.2903°, -82.7278°), and Green Key (28.2530°, -82.7496°) sites on the Gulf of Mexico coastline in Pasco County, Florida, USA. At each site, we established three plot pairs, each consisting of one saltmarsh plot and one mangrove plot. Plots were 50 m^2 in size. Plots pairs within a site were separated by 230-1070 m, and the mangrove and saltmarsh plots composing a pair were 70-170 m apart. All plot pairs consisted of directly adjacent patches of mangrove forest and J. roemerianus saltmarsh, with the mangrove forests exhibiting a closed canopy and a tree architecture (height 4-6 m, crown width 1.5-3 m). Mangrove plots were located at approximately the midpoint between the seaward edge (water-mangrove interface) and landward edge (mangrove-marsh interface) of the mangrove zone. Saltmarsh plots were located 20-25 m away from any mangrove trees and into the J. roemerianus zone (i.e., landward from the mangrove-marsh interface). Plot pairs were coarsely similar in geomorphic setting, as all were located on the Gulf of Mexico coastline, rather than within major sheltering formations like Tampa Bay, and all plot pairs fit the tide-dominated domain of the Woodroffe classification (Woodroffe, 2002, "Coasts: Form, Process and Evolution", Cambridge University Press), given their conspicuous semi-diurnal tides. There was nevertheless some geomorphic variation, as some plot pairs were directly open to the Gulf of Mexico while others sat behind keys and spits or along small tidal creeks. Our use of a plot-pair approach is intended to control for this geomorphic variation. Plot center elevations (cm above mean sea level, NAVD 88) were estimated by overlaying the plot locations determined with a global positioning system (Garmin GPS 60, Olathe, KS, USA) on a LiDAR-derived bare-earth digital elevation model (Dewberry, Inc., 2019). The digital elevation model had a vertical accuracy of ± 10 cm (95 % CI) and a horizontal accuracy of ± 116 cm (95 % CI). Soil samples were collected via coring at low tide in June 2011. From each plot, we collected a composite soil sample consisting of three discrete 5.1 cm diameter soil cores taken at equidistant points to 7.6 cm depth. Cores were taken by tapping a sleeve into the soil until its top was flush with the soil surface, sliding a hand under the core, and lifting it up. Cores were then capped and transferred on ice to our laboratory at the University of South Florida (Tampa, Florida, USA), where they were combined in plastic zipper bags, and homogenized by hand into plot-level composite samples on the day they were collected. A damp soil subsample was immediately taken from each composite sample to initiate 1 y incubations for determination of active C and N (see below). The remainder of each composite sample was then placed in a drying oven (60 °C) for 1 week with frequent mixing of the soil to prevent aggregation and liberate water. Organic wetland soils are sometimes dried at 70 °C, however high drying temperatures can volatilize non-water liquids and oxidize and decompose organic matter, so 50 °C is also a common drying temperature for organic soils (Gardner 1986, "Methods of Soil Analysis: Part 1", Soil Science Society of America); we accordingly chose 60 °C as a compromise between sufficient water removal and avoidance of non-water mass loss. Bulk density was determined as soil dry mass per core volume (adding back the dry mass equivalent of the damp subsample removed prior to drying). Dried subsamples were obtained for determination of soil organic matter (SOM), mineral texture composition, and extractable and total carbon (C) and nitrogen (N) within the following week. Sample analyses. A dried subsample was apportioned from each composite sample to determine SOM as mass loss on ignition at 550 °C for 4 h. After organic matter was removed from soil via ignition, mineral particle size composition was determined using a combination of wet sieving and density separation in 49 mM (3 %) sodium hexametaphosphate ((NaPO_3)_6) following procedures in Kettler et al. (2001, Soil Science Society of America Journal 65, 849-852). The percentage of dry soil mass composed of silt and clay particles (hereafter, fines) was calculated as the mass lost from dispersed mineral soil after sieving (0.053 mm mesh sieve). Fines could have been slightly underestimated if any clay particles were burned off during the preceding ignition of soil. An additional subsample was taken from each composite sample to determine extractable N and organic C concentrations via 0.5 M potassium sulfate (K_2SO_4) extractions. We combined soil and extractant (ratio of 1 g dry soil:5 mL extractant) in plastic bottles, reciprocally shook the slurry for 1 h at 120 rpm, and then gravity filtered it through Fisher G6 (1.6 μm pore size) glass fiber filters, followed by colorimetric detection of nitrite (NO_2^-) + nitrate (NO_3^-) and ammonium (NH_4^+) in the filtrate (Hood Nowotny et al., 2010,Soil Science Society of America Journal 74, 1018-1027) using a microplate spectrophotometer (Biotek Epoch, Winooski, VT, USA). Filtrate was also analyzed for dissolved organic C (referred to hereafter as extractable organic C) and total dissolved N via combustion and oxidation followed by detection of the evolved CO_2 and N oxide gases on a Formacs HT TOC/TN analyzer (Skalar, Breda, The Netherlands). Extractable organic N was then computed as total dissolved N in filtrate minus extractable mineral N (itself the sum of extractable NH_4-N and NO_2-N + NO_3-N). We determined soil total C and N from dried, milled subsamples subjected to elemental analysis (ECS 4010, Costech, Inc., Valencia, CA, USA) at the University of South Florida Stable Isotope Laboratory. Median concentration of inorganic C in unvegetated surface soil at our sites is 0.5 % of soil mass (Anderson, 2019, Univ. of South Florida M.S. thesis via methods in Wang et al., 2011, Environmental Monitoring and Assessment 174, 241-257). Inorganic C concentrations are likely even lower in our samples from under vegetation, where organic matter would dilute the contribution of inorganic C to soil mass. Nevertheless, the presence of a small inorganic C pool in our soils may be counted in the total C values we report. Extractable organic C is necessarily of organic C origin given the method (sparging with HCl) used in detection. Active C and N represent the fractions of organic C and N that are mineralizable by soil microorganisms under aerobic conditions in long-term soil incubations. To quantify active C and N, 60 g of field-moist soil were apportioned from each composite sample, placed in a filtration apparatus, and incubated in the dark at 25 °C and field capacity moisture for 365 d (as in Lewis et al., 2014, Ecosphere 5, art59). Moisture levels were maintained by frequently weighing incubated soil and wetting them up to target mass. Daily CO_2 flux was quantified on 29 occasions at 0.5-3 week intervals during the incubation period (with shorter intervals earlier in the incubation), and these per day flux rates were integrated over the 365 d period to compute an estimate of active C. Observations of per day flux were made by sealing samples overnight in airtight chambers fitted with septa and quantifying headspace CO_2 accumulation by injecting headspace samples (obtained through the septa via needle and syringe) into an infrared gas analyzer (PP Systems EGM 4, Amesbury, MA, USA). To estimate active N, each incubated sample was leached with a C and N free, 35 psu solution containing micronutrients (Nadelhoffer, 1990, Soil Science Society of America Journal 54, 411-415) on 19 occasions at increasing 1-6 week intervals during the 365 d incubation, and then extracted in 0.5 M K_2SO_4 at the end of the incubation in order to remove any residual mineral N. Active N was then quantified as the total mass of mineral N leached and extracted. Mineral N in leached and extracted solutions was detected as NH_4-N and NO_2-N + NO_3-N via colorimetry as above. This incubation technique precludes new C and N inputs and persistently leaches mineral N, forcing microorganisms to meet demand by mineralizing existing pools, and thereby directly assays the potential activity of soil organic C and N pools present at the time of soil sampling. Because this analysis commences with disrupting soil physical structure, it is biased toward higher estimates of active fractions. Calculations. Non-mobile C and N fractions were computed as total C and N concentrations minus the extractable and active fractions of each element. This data package reports surface-soil constituents (moisture, fines, SOM, and C and N pools and fractions) in both gravimetric units (mass constituent / mass soil) and areal units (mass constituent / soil surface area integrated through 7.6 cm soil depth, the depth of sampling). Areal concentrations were computed as X × D × 7.6, where X is the gravimetric concentration of a soil constituent, D is soil bulk density (g dry soil / cm^3), and 7.6 is the sampling depth in cm. 
    more » « less
  2. Abstract

    Soil erosion diminishes agricultural productivity by driving the loss of soil organic carbon (SOC). The ability to predict SOC redistribution is important for guiding sustainable agricultural practices and determining the influence of soil erosion on the carbon cycle. Here, we develop a landscape evolution model that couples soil mixing and transport to predict soil loss and SOC patterns within agricultural fields. Our reduced complexity numerical model requires the specification of only two physical parameters: a plow mixing depth,Lp, and a hillslope diffusion coefficient,D. Using topography as an input, the model predicts spatial patterns of surficial SOC concentrations and complex 3D SOC pedostratigraphy. We use soil cores from native prairies to determine initial SOC‐depth relations and the spatial pattern of remote sensing‐derived SOC in adjacent agricultural fields to evaluate the model predictions. The model reproduces spatial patterns of soil loss comparable to those observed in satellite images. Our results indicate that the distribution of soil erosion and SOC in agricultural fields can be predicted using a simple geomorphic model where hillslope diffusion plays a dominant role. Such predictions can aid estimates of carbon burial and evaluate the potential for future soil loss in agricultural landscapes.

     
    more » « less
  3. Complete transformations of land cover from prairie, wetlands, and hardwood forests to row crop agriculture and urban centers are thought to have caused profound changes in hydrology in the Upper Midwestern US since the 1800s. In this study, we investigate four large (23 000–69 000 km2) Midwest river basins that span climate and land use gradients to understand how climate and agricultural drainage have influenced basin hydrology over the last 79 years. We use daily, monthly, and annual flow metrics to document streamflow changes and discuss those changes in the context of precipitation and land use changes. Since 1935, flow, precipitation, artificial drainage extent, and corn and soybean acreage have increased across the region. In extensively drained basins, we observe 2 to 4 fold increases in low flows and 1.5 to 3 fold increases in high and extreme flows. Using a water budget, we determined that the storage term has decreased in intensively drained and cultivated basins by 30–200 % since 1975, but increased by roughly 30 % in the less agricultural basin. Storage has generally decreased during spring and summer months and increased during fall and winter months in all watersheds. Thus, the loss of storage and enhanced hydrologic connectivity and efficiency imparted by artificial agricultural drainage appear to have amplified the streamflow response to precipitation increases in the Midwest. Future increases in precipitation are likely to further intensify drainage practices and increase streamflows. Increased streamflow has implications for flood risk, channel adjustment, and sediment and nutrient transport and presents unique challenges for agriculture and water resource management in the Midwest. Better documentation of existing and future drain tile and ditch installation is needed to further understand the role of climate versus drainage across multiple spatial and temporal scales. 
    more » « less
  4. Abstract

    Dynamic Energy Budget models relate whole organism processes such as growth, reproduction and mortality to suborganismal metabolic processes. Much of their potential derives from extensions of the formalism to describe the exchange of metabolic products between organisms or organs within a single organism, for example the mutualism between corals and their symbionts. Without model simplification, such models are at risk of becoming parameter-rich and hence impractical. One natural simplification is to assume that some metabolic processes act on ‘fast’ timescales relative to others. A common strategy for formulating such models is to assume that ‘fast’ processes equilibrate immediately, while ‘slow’ processes are described by ordinary differential equations. This strategy can bring a subtlety with it. What if there are multiple, interdependent fast processes that have multiple equilibria, so that additional information is needed to unambiguously specify the model dynamics? This situation can easily arise in contexts where an organism or community can persist in a ‘healthy’ or an ‘unhealthy’ state with abrupt transitions between states possible. To approach this issue, we offer the following: (a) a method to unambiguously complete implicitly defined models by adding hypothetical ‘fast’ state variables; (b) an approach for minimizing the number of additional state variables in such models, which can simplify the numerical analysis and give insights into the model dynamics; and (c) some implications of the new approach that are of practical importance for model dynamics, e.g. on the bistability of flux dynamics and the effect of different initialization choices on model outcomes. To demonstrate those principles, we use a simplified model for root-shoot dynamics of plants and a related model for the interactions between corals and endosymbiotic algae that describes coral bleaching and recovery.

     
    more » « less
  5. Abstract

    For evaluating the climatic and landscape controls on long‐term baseflow, baseflow index (BFI, defined as the ratio of baseflow to streamflow) and baseflow coefficient (BFC, defined as the ratio of baseflow to precipitation) are formulated as functions of climate aridity index, storage capacity index (defined as the ratio of average soil water storage capacity to precipitation), and a shape parameter for the spatial variability of storage capacity. The derivation is based on the two‐stage partitioning framework and a cumulative distribution function for storage capacity. Storage capacity has a larger impact on BFI than on BFC. When storage capacity index is smaller than 1, BFI is less sensitive to storage capacity index in arid regions compared to that in humid regions; whereas, when storage capacity index is larger than 1, BFI is less sensitive to storage capacity index in humid regions. The impact of storage capacity index on BFC is only significant in humid regions. The shape parameter plays an important role on fast flow generation at the first‐stage partitioning in humid regions and baseflow generation at the second‐stage partitioning in arid regions. The derived formulae were applied to more than 400 catchments where storage capacity index was found to follow a logarithmic function with climate aridity index. The role of climate forcings at finer timescales on baseflow were quantified, indicating that seasonality in climate forcings has a significant control especially on BFI.

     
    more » « less