A major obstacle to selecting the most appropriate crops andclosing the yield gap in many areas of the world is a lack of site-specificsoil information. Accurate information on soil properties is critical foridentifying soil limitations and the management practices needed to improvecrop yields. However, acquiring accurate soil information is often difficultdue to the high spatial and temporal variability of soil properties at finescales and the cost and inaccessibility of laboratory-based soil analyses.With recent advancements in predictive soil mapping, there is a growingexpectation that soil map predictions can provide much of the informationneeded to inform soil management. Yet, it is unclear how accurate currentsoil map predictions are at scales relevant to management. The mainobjective of this study was to address this issue by evaluating thesite-specific accuracy of regional-to-global soil maps, using Ghana as atest case. Four web-based soil maps of Ghana were evaluated using a datasetof 6514 soil profile descriptions collected on smallholder farms using theLandPKS mobile application. Results from this study revealed that publiclyavailable soil maps in Ghana lack the needed accuracy (i.e., correctidentification of soil limitations) to reliably inform soil managementdecisions at the 1-2 ha scale common to smallholders. Standard measures ofmap accuracy for soil texture class and rock fragment class predictionsshowed that all soil maps had similar performance in estimating the correctproperty class. Overall soil texture class accuracies ranged from 8 %-14 %but could be as high as 38 %-64 % after accounting for uncertainty in theevaluation dataset. Soil rock fragment class accuracies ranged from26 %-29 %. However, despite these similar overall accuracies, there weresubstantial differences in soil property predictions among the four maps,highlighting that soil map errors are not uniform between maps. To betterunderstand the functional implications of these soil property differences,we used a modified version of the FAO Global Agro-Ecological Zone (GAEZ)soil suitability modeling framework to derive soil suitability ratings foreach soil data source. Using a low-input, rain-fed, maize productionscenario, we evaluated the functional accuracy of map-based soil propertyestimates. This analysis showed that soil map data significantlyoverestimated crop suitability for over 65 % of study sites, potentiallyleading to ineffective agronomic investments by farmers, includingcash-constrained smallholders.

High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, SoilGrids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to the number, types, and spatial distribution of texture classes. The topsoil texture information provided by the maps is thus sufficiently inaccurate that it would result in significant errors in a number of applications, including irrigation system design and predictions of potential forage and crop productivity, water runoff, and soil erosion. Clearly, the use of these existing maps for policy- and decision-making is highly questionable and there is a critical need for better on-site estimates and soil map predictions. We propose that mobile apps, citizen science, and crowdsourcing can help meet this need.

Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision-making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on-the-ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal-explicit, pixel-level estimates of uncertainty. We evaluated the model with 5,780 on-the-ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel-level uncertainty. The new product is available on the Rangeland Analysis Platform (), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on-the-ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision-making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

Accurately identifying the soil map unit component at a specific point-location within a landscape is critical for implementing sustainable soil management. Recent developments in smartphone-based technologies for characterizing soil profiles, coupled with improved numerical soil classification algorithms, have made it more accessible for non-soil scientists to sample, characterize, and classify soil profiles. The main objective of this study was to evaluate an operational soil classification framework for identifying the soil component at a sampling-location based on the numerical similarity of soil property values between the sampled soil profile and the soil components mapped in that area. To evaluate this soil identification framework, we used a subset of the U.S. National Cooperative Soil Survey Soil Characterization Database (NCSS-SCD) as our soil profile test dataset and the U.S. Soil Survey Geographic (SSURGO) database as our reference dataset using profile data of soil components in the area surrounding each test profile. Numerical similarity was tested using soil property data representing different degrees of generalization, both in terms of generalizing depth-wise variability (i.e., depth-support) and generalizing across feature space (i.e., soil properties). Three soil property groups (i.e., Novice, Expert, Expert-Plus) representing different levels of detail and three types of depth-support (i.e., genetic horizon, depth intervals, and depth functions) were evaluated. Using a simple set of soil property inputs (i.e., Novice: soil texture class, rock fragment volume class, and soil color) resulted in nearly as high identification accuracy (46-53%) as that achieved with an Expert (48-57%) dataset that included more precise determinations (percent sand, silt, clay, and rock fragment volume), and virtually no further improvement with the addition of pH and organic matter in the Expert-Plus dataset (53-60%). This study also showed minimal effect from the type of depth-support used to represent depth-wise variability. Furthermore, we evaluated several measures of soil functional similarity (i.e., ecological sites, land capability, taxonomic distance) which resulted in management relevant accuracies ranging from 65-89%. These findings support the utility of simple soil observations sampled at fixed depths for soil identification.

The response hierarchy of "Avoid > reduce > reverse" is increasingly acknowledged as the best strategy for prioritizing actions designed to address land degradation at hectare to national scales. This hierarchy is based on the assumption that the economic return on investment (ROI) will usually be higher for actions that help avoid degradation than for those required to restore already degraded land. While a useful first step, the hierarchy fails to account for how differences in land potential, defined as its potential to sustainably generate ecosystem services, may affect the ROI of actions at each level of the response hierarchy. The objective of this paper is to present a strategy for improving ROI at the landscape scale and above by systematically applying a more holistic understanding of land potential to the identification and prioritization of land investments. This objective is addressed in three sections. The first section explains how the potential short- and long-term resistance and resilience of the land can be used together with its potential productivity to prioritize actions designed to avoid, reduce and reverse degradation. In the second section we explain how this prioritization can be further optimized based on an understanding of degradation risk as indicated by the land's potential to generate relatively high short-term profits under management systems that are likely to increase degradation, or result in degradation of restored land. This potential, and land managers' perception of it, depend on a wide variety of factors including markets, infrastructure, and access to technology. Together these first two sections provide a framework for increasing ROI, while reducing the risk of failure at hectare to national scales. In the final section we briefly describe the Land-Potential Knowledge System (LandPKS), a modular mobile app that makes it possible for virtually anyone with a smartphone to make the land potential determinations necessary to apply the framework described in the first two sections.

Ecological site descriptions (ESDs) and associated state-and-transition models (STMs) provide a nationally consistent classification and information system for defining ecological land units for management applications in the United States. Current spatial representations of ESDs, however, occur via soil mapping and are therefore confined to the spatial resolution used to map soils within a survey area. Land management decisions occur across a range of spatial scales and therefore require ecological information that spans similar scales. Digital mapping provides an approach for optimizing the spatial scale of modeling products to best serve decision makers and have the greatest impact in addressing land management concerns. Here, we present a spatial modeling framework for mapping ecological sites using machine learning algorithms, soil survey field observations, soil survey geographic databases, ecological site data, and a suite of remote sensing-based spatial covariates (e.g., hyper-temporal remote sensing, terrain attributes, climate data, land-cover, lithology). Based on the theoretical association between ecological sites and landscape biophysical properties, we hypothesized that the spatial distribution of ecological sites could be predicted using readily available geospatial data. This modeling approach was tested at two study areas within the western United States, representing 6.1 million ha on the Colorado Plateau and 7.5 million ha within the Chihuahuan Desert. Results show our approach was effective in mapping grouped ecological site classes (ESGs), with 10-fold cross-validation accuracies of 70% in the Colorado Plateau based on 1405 point observations across eight expertly-defined ESG classes and 79% in the Chihuahuan Desert based on 2589 point observations across nine expertly-defined ESG classes. Model accuracies were also evaluated using external-validation datasets; resulting in 56 and 44% correct classification for the Colorado Plateau and Chihuahuan Desert, respectively. National coverage of the training and covariate data used in this study provides opportunities for a consistent national-scale mapping effort of ecological sites.

The assessment and monitoring of soil disturbance and its effect on soil quality (i.e., ability to support a range of ecosystem services) has been hindered due to the shortcomings of many traditional analytical techniques, including high cost, long-term time investment and difficulties with data interpretation. Consequently, there is a critical need to develop rapid and repeatable approaches for quantifying changes in soil quality that land managers may use to assess the condition and trend of natural and managed ecosystems. Here we report on a rapid, high throughput approach using fingerprint Fourier transformed infrared (FTIR) spectroscopy and chemometric modeling. Fingerprint FTIR incorporates all information embedded within the FTIR spectrum, thus producing a biogeochemical or ecological "fingerprint" of the soil. This methodology was applied in a highly disturbed forest ecosystem over a 19-year sampling period to detect, via spectral analysis, changes in dynamic soil properties (e.g., soil organic matter and reactive mineralogy) that can indicate changes in soil quality. Two chemometric statistical techniques (i.e., hierarchical clustering analysis [HCA] and discriminate analysis of principal components [DAPC]) were evaluated for interpreting and quantifying similarities/dissimilarities between samples utilizing the entire FTIR spectra from each sample. We found that both statistical approaches provided a means for clearly discriminating between degraded soils, soils in recovery, and reference soils. DAPC analysis provided additional information on the spectral wavenumbers most important in differentiating between soil samples across our disturbance time series. Wavenumbers relating to stretching of phenolic (C-O), aromatic (C=C, C-C), carboxylic acids (C=O), and to a lesser extent aliphatic (C-H) bonds were important in discriminating between soil samples. These results confirmed our visual interpretation of FTIR spectra, where an increase in aliphatic (fats and lipids) and decrease in phenolic/aromatic (lignin) absorption occurred following intensive site disturbance, followed by a return to predisturbance absorption values 17 years later. Results from this study illustrate the potential of fingerprint FTIR and chemometrics as an efficient technique for quantifying changes in soil quality that may be used in the monitoring and assessment of soil landscape change. Additional research across a range of soil ecosystems and under different types of ecosystem disturbance is needed to further validate this approach and to demonstrate its wide applicability as a land management tool.

A common phenomenon observed in natural and constructed wetlands is short-circuiting of flow and formation of stagnant zones that are only indirectly connected with the incoming water. Biogeochemistry of passive areas is potentially much different than that of active zones. In the research reported in this paper, the spatial resolution of a previously developed wetland nutrient cycling model was improved in order to capture the spatial variability of concentrations and reaction rates regarding nitrogen and carbon cycles throughout active and passive zones of wetlands. The upgraded model allows for several compartments in the horizontal domain, with all neighboring compartments connected through advective and dispersive/diffusive mass transport. The model was applied to data collected from a restored wetland in California that was characterized by the formation of a large stagnant zone at the southern end of the wetland due to close vicinity of the inlet and outlet structures in the northern end. Mass balance analysis revealed that over the course of the research period, about 23.4±3.9% of the incoming total nitrogen load was removed or retained by the wetland. It was observed that mass of all exchanges (physical and biogeochemical) regarding nitrogen cycling decreased along the activity gradient from active to passive zones. Model results also revealed that anaerobic processes become more significant along the activity gradient towards passive areas.

Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral ‘fingerprint’ of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches.

Indices derived from remotely-sensed imagery are commonly used to predict soil properties with digital soil mapping (DSM) techniques. The use of images from single dates or a small number of dates is most common for DSM; however, selection of the appropriate images is complicated by temporal variability in land surface spectral properties. We argue that hyper-temporal remote sensing (RS) (i.e., hundreds of images) can provide novel insights into soil spatial variability by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral ‘fingerprint’ of the soil-vegetation relationship which is directly related to a range of soil properties. To evaluate the hyper-temporal RS approach, this study first reviewed and synthesized, within the context of temporal variability, previous research that has used RS imagery for DSM. Results from this analysis support the notion that temporal variability in RS spectra, as driven by soil and climate feedbacks, is an important predictor of soil variability. To explicitly evaluate this idea and to demonstrate the utility of the hyper-temporal approach, we present a case study in a semiarid landscape of southeastern Arizona, USA. In this case study surface soil texture and coarse fragment classes were predicted using a 28year time series of Landsat TM derived normalized difference vegetation index (NDVI) and modeled using support vector machine (SVM) classification, and results evaluated relative to more traditional RS approaches (e.g., mono-, bi-, and multi-temporal). Results from the case study show that SVM classification using hyper-temporal RS imagery was more effective in modeling both soil texture and coarse fragment classes relative to mono-, bi-, or multi-temporal RS, with classification accuracies of 67% and 62%, respectively. Short-term transitions between wet and dry periods (i.e., <6months) were the dominant drivers of vegetation spectral variability and corresponded to the general timing of significant RS scenes within in our SVM models, confirming the importance of spectral variability in predicting soil texture and coarse fragment classes. Results from the case study demonstrate the efficacy of the hyper-temporal RS approach in predicting soil properties and highlights how hyper-temporal RS can improve current methods of soil mapping efforts through its ability to characterize subtle changes in RS spectra relating to variation in soil properties.

Frequency and severity of extreme climatic events are forecast to increase in the 21st century. Predicting how managed ecosystems may respond to climatic extremes is intensified by uncertainty associated with knowing when, where, and how long effects of extreme events will be manifest in an ecosystem. In water-limited ecosystems with high inter-annual variability in rainfall, it is important to be able to distinguish responses that result from seasonal fluctuations in rainfall from long-term directional increases or decreases in precipitation. A tool that successfully distinguishes seasonal from directional biomass responses would allow land managers to make informed decisions about prioritizing mitigation strategies, allocating human resource monitoring efforts, and mobilizing resources to withstand extreme climatic events. We leveraged long-term observations (2000-2013) of quadrat-level plant biomass at multiple locations across a semiarid landscape in southern New Mexico to verify the use of Normalized Difference Vegetation Index (NDVI) time series derived from 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data as a proxy for changes in aboveground productivity. This period encompassed years of sustained drought (2000-2003) and record-breaking high rainfall (2006 and 2008) followed by subsequent drought years (2011 through 2013) that resulted in a restructuring of plant community composition in some locations. Our objective was to decompose vegetation patterns derived from MODIS NDVI over this period into contributions from (1) the long-term trend, (2) seasonal cycle, and (3) unexplained variance using the Breaks for Additive Season and Trend (BFAST) model. BFAST breakpoints in NDVI trend and seasonal components were verified with field-estimated biomass at 15 sites that differed in species richness, vegetation cover, and soil properties. We found that 34 of 45 breaks in NDVI trend reflected large changes in mean biomass and 16 of 19 seasonal breaks accompanied changes in the contribution to biomass by perennial and/or annual grasses. The BFAST method using satellite imagery proved useful for detecting previously reported ground-based changes in vegetation in this arid ecosystem. We demonstrate that time series analysis of NDVI data holds potential for monitoring landscape condition in arid ecosystems at the large spatial scales needed to differentiate responses to a changing climate from responses to seasonal variability in rainfall.

Growing concern over climate and management induced changes to soil nutrient status has prompted interest in understanding the spatial distribution of forest soil properties. Recent advancements in remotely sensed geospatial technologies are providing an increasing array of data sources (e.g., LiDAR, hyper spectral imagery) relating to forest biophysical properties. While these data sources have the potential to improve spatial predictions of forest soil properties, considerable uncertainty exists regarding which remotely sensed (RS) indices are correlated to soil variability and what underlying pedogenic processes connect them. The main objective of this study was to identify and interpret RS indices that account for soil variability within a 2300 ha forested watershed. Redundancy analysis (RDA) and variation partitioning methods were used to uncouple the complexity of soil-environmental relationships. Thirty-two soil pedons were described, sampled, characterized and analyzed for 22 soil properties within the 0-50 cm soil depth interval. A suite of environmental covariates, comprised of LiDAR derived canopy metrics, land-surface and hydrologic terrain indices, broad-band remotely sensed indices (GeoEye-1), and narrow-band hyper-spectral indices (HyMap), were used as covariates in our RDA models. Principal coordinates of neighbor matrices (PCNM) was used to disentangle the contribution of spatial autocorrelation among sampling locations to the total variance explained by our RDA models. Two groups of soil properties were identified using discriminate analysis of principal components, with each soil property group (SPG) relating to different pedogenic processes occurring with the watershed (SPG1: organic matter metal cycling; SPG2: base-cation cycling). Our results show there was a relatively strong correspondence between soil properties and terrain/spectral indices; with 61% and 81% of the total variance explained by the first four RDA axes for SPG1 and SPG2, respectively. Variation partitioning analysis revealed that both SPG1 and SPG2 were most strongly related to terrain and canopy indices; although spectral indices were also important, especially for SGP2. Variation in the types of RS indices correlated to each SPG results from variation in the degree to which each environmental covariate relates to the pedogenic process (es) driving soil property development. The approach used in this study can help improve our understanding of soil spatial variability through identifying the most significant environmental covariates related to soil variation. Given the growing demands placed upon forest ecosystems (e.g., timber, recreation, carbon sequestration), improved knowledge of soil variability and the factors that affect the soil resource is essential to facilitate more effective forest management. Published by Elsevier B.V.

Arid rangelands have been severely degraded over the past century. Multi-temporal remote sensing techniques are ideally suited to detect significant changes in ecosystem state; however, considerable uncertainty exists regarding the effects of changing image resolution on their ability to detect ecologically meaningful change from satellite time-series.

Constructed and restored wetlands are a common practice to filter agricultural runoff, which often contains high levels of pollutants, including nitrate. Seepage waters from wetlands have potential to contaminate groundwater. This study used soil and water monitoring and hydrologic and nitrogen mass balances to document the fate and transport of nitrate in seepage and surface waters from a restored flow-through wetland adjacent to the San Joaquin River, California. A 39% reduction in NO3-N concentration was observed between wetland surface water inflows (12.87±6.43mgL−1; mean±SD) and outflows (7.87±4.69mgL−1). Redox potentials were consistently below the nitrate reduction threshold (∼250mV) at most sites throughout the irrigation season. In the upper 10cm of the main flowpath, denitrification potential (DNP) for soil incubations significantly increased from 151 to 2437mgNO3-Nm−2d−1 when nitrate was added, but showed no response to carbon additions indicating that denitrification was primarily limited by nitrate. Approximately 72% of the water entering the wetland became deep seepage, water that percolated beyond 1-m depth. The wetland was highly effective at removing nitrate (3866kgNO3-N) with an estimated 75% NO3-N removal efficiency calculated from a combined water and nitrate mass balance. The mass balance results were consistent with estimates of NO3-N removed (5085kgNO3-N) via denitrification potential. Results indicate that allowing seepage from wetlands does not necessarily pose an appreciable risk for groundwater nitrate contamination and seepage can facilitate greater nitrate removal via denitrification in soil compared to surface water transport alone.

Quantifying the spatial distribution of soil properties is essential for ecological and environmental modeling at the landscape scale. Terrain attributes are among the primary covariates in soil-landscape models due to their control on energy and mass fluxes, which in turn control the spatial distribution of soil properties and processes. While numerous studies have demonstrated the importance of terrain attributes for predicting landscape-scale soil variability, considerable uncertainty exists as to the scale-dependency of light detection and ranging (LiDAR) derived terrain attributes on the accuracy of soil-landscape model predictions. Thirty five pedons were sampled by genetic horizon in a 2300 ha forested watershed and three soil properties (clay, sum of bases, and total carbon), representing dominant pedogenic processes within the watershed were analyzed. Soil properties were used as dependent variables and terrain attributes, calculated from LiDAR derived DEMs of various grid resolutions (ranging from 5 to 50 m) and neighborhood extents (ranging from 15 to 350 m), were used as predictor variables in ordinary least-squares (OLS) regression models. Results from this study show that model predictions exhibit a strong scale-dependency, with percent clay, sum of bases, and total carbon having the highest R2-adj and lowest root mean square error (RMSE) at coarse neighborhood extents (i.e., 150 to 300 m) both between soil variables and across soil depths. Furthermore, in certain instances grid resolution was also shown to affect soil–terrain correlations, although to a lesser degree than neighborhood extent. In many cases fine to moderate scale grid resolutions (i.e.,< 30 m) more accurately represented terrain features, resulting in higher correlations to soil properties at fixed neighborhood extents relative to course grid resolutions. Additionally, these results show that fine scale topographic information (i.e., 1 to 5 m) does not necessarily provide a stronger predictor of soil spatial variability relative to moderate scale information. This study provides a robust framework for investigating pedogeomorphological processes on a landscape scale through examination of the scale dependency of modeled terrain attributes in quantitative soil-landscape modeling.

Wetland environments are important sites for the cycling and retention of terrestrially derived organic matter and nutrients. Wetland treatment of agricultural runoff has been shown to improve water quality and promote carbon sequestration. However, the potential role of eutrophic wetlands as a source of algal loading contributing to downstream hypoxia has prompted interest in understanding algal productivity and export from these systems. This study, in the San Joaquin Valley, California, quantified a mass balance of carbon and nutrients within a seasonally-saturated constructed wetland receiving agricultural runoff, as well as quantifying autochthonous carbon production on four sampling dates during a year with minimal emergent vegetation. Results from this study show that the wetland was a net-sink for nutrients and particulate/dissolved organic carbon. Despite high concentrations of inflowing nutrients and high rates of primary productivity, high respiration rates limited net organic C production and export due to high heterotrophic activity. The addition of high C loads in inflowing water and moderate retention efficiencies, however, resulted in a positive C retention during most sampling dates. This study provides valuable insight into the connection between elevated carbon and nutrient inflows, their effects on autochthonous carbon production, and resulting carbon and nutrient outflows.

A. Sharifi, L. Kalin, M. M. Hantush, J. J. Maynard, A. T. O’Geen and R. A. Dahlgren 1 School of Forestry and wildlife Sciences, Auburn University, 602 Duncan Dr., Auburn, AL 36849; Sharifi@auburn.edu 2 School of Forestry and wildlife Sciences, Auburn University, 602 Duncan Dr., Auburn, AL 36849; Latif@auburn.edu 3 National Risk Management Research Laboratory, U.S. Environmental Protection Agency, 26 West martin Luther King Dr.,Cincinnati, OH 45268; hantush.mohamed@epa.gov 4 USDA ARS Jornada Experimental Range, P.O. Box 30003, MSC 3JER, New Mexico State University, Las Cruces, NM 88003; jmaynard@nmsu.edu 5 Department of Land, Air and Water Resources, University of California Davis, One Shields Ave., Davis, CA 95616; atogeen@ucdavis.edu 6 Department of Land, Air and Water Resources, University of California Davis, One Shields Ave., Davis, CA 95616; radahlgren@ucdavis.edu

Wetlands are highly dynamic ecosystems that exhibit large temporal and spatial gradients in biogeochemical and metabolic processes. While previous studies have documented temporal variability of wetland metabolism, very little is known regarding how wetland metabolism varies spatially. This is particularly relevant for constructed flow-through wetlands used to treat agricultural runoff due to growing concern over the potential for these systems to act as incubators of biological oxygen demanding (BOD) substances (i.e., algal biomass) and contributors to downstream hypoxia. This study employed a spatially explicit characterization of wetland water column properties using multiparameter water quality sondes, thus allowing the calculation of wetland metabolism across a range of spatial scales (meters to 100 s of meters). This study documented a high degree of spatial variability in metabolic rates within the wetland, with average gross primary productivity (GPP) ranging from 6.41 to 13.69 g O-2 m(-2) d(-1), community respiration (CR) ranging from 4.85 to 9.79 g O-2 m(-2) d(-1), and net ecosystem production (NEP) ranging from 0.35 to 2.42 g O-2 m(-2) d(-1). Linkages between metabolic rates and environmental controls were quantified using a spatial/temporal statistical approach. Five key environmental parameters (turbidity, dissolved reactive phosphorus, ammonium, electrical conductivity, and temperature) were found to correlate with ecosystem metabolism. Results from this study show that despite high rates of primary productivity, high respiration rates limited NEP in this wetland due to high heterotrophic activity. Published by Elsevier B.V.

Abstract. The fate of organic carbon (C) lost by erosion is not well understood in agricultural settings. Recent models suggest that wetlands and other small water bodies may serve as important long-term sinks of eroded C, receiving ~30 % of all eroded material in the US. To better understand the role of seasonally-saturated wetlands in sequestering eroded C, we examined the spatial and temporal dynamics of C and sediment accumulation in a 13-yr-old constructed wetland used to treat agricultural runoff. The fate of C sequestered within deposited sediment was modeled using point-sampling, remote sensing, and geostatistics. Using a spatially-explicit sampling design, annual net rates of sedimentation and above ground biomass were measured during two contrasting years (vegetated, 2004 vs. non-vegetated, 2005), followed by collection of sediment cores to the antecedent soil layer, representing 13 yr of sediment and C accumulation. We documented high annual variation in the relative contribution of endogenous and exogenous C sources, as well as absolute rates of sediment and C deposition. This annual variation, however, was muted in the long-term (13-yr) sediment record, which showed consistent vertical patterns of uniform C distribution (~14 g kg−1) and δ13C signatures in high depositional environments. This was in contrast to low depositional environments which had high levels of surface C enrichment (20–35 g kg−1) underlain by C depleted (5–10 g kg−1) sediments and an increasing δ13C signature with depth indicating increasing decomposition. These results highlight the importance of sedimentation in physically protecting soil organic carbon and its role in controlling the long-term C concentration of seasonally-saturated wetland soils. While significant enrichment of surface sediments with endogenous C occurred in newly deposited sediment (i.e., 125 kg m2 in 2004), fluctuating cycles of flooding and drying maintained the long-term C concentration at the same level as inflowing sediment (i.e., 14 g kg−1), indicating no additional long-term storage of endogenous C. These results demonstrate that constructed flow-through wetlands can serve as important sinks for eroded C and sediment in agricultural landscapes, however, additional C sequestration via enrichment from endogenous sources may be limited in seasonally-saturated wetlands due to rapid decomposition during drying cycles.