Sabine Grunwald

Assessment of the spatial distribution of soil properties in a northern everglades marsh

Florida Everglades restoration plans are aimed at maintaining and restoring characteristic landscape features such as soil, vegetation, and hydrologic patterns. This study presents the results from an exhaustive spatial sampling of key soil properties in Water Conservation Area 1 (WCA 1), which is part of the northern Everglades. Three soil strata were sampled: floc, upper 0- to 10-cm soil layer, and 10- to 20-cm soil layer. A variety of properties were measured including bulk density (BD), loss on ignition (LOI), total phosphorus (TP), total inorganic phosphorus (TIP), total nitrogen (TN), total carbon (TC), total iron (TFe), total magnesium (TMg), total aluminum (TAl), and total calcium (TCa). Interpolated maps and model prediction uncertainties of properties were generated using geostatistical methods. We found that the uncertainty associated with spatial predictions of floc, particularly floc BD, was highest, whereas spatial predictions of soil chemical properties such as soil Ca were more accurate. The resultant spatial patterns for these soil properties identified three predominant features in WCA 1: (i) a north to south gradient in soil properties associated with the predominant hydrological gradient, (ii) areas of considerable soil nutrient enrichment along the western canal of WCA 1, and (iii) areas of considerable Fe enrichment along the eastern canal. By using geostatistical techniques we were able to describe the spatial dynamics of soil variables and express these predictions with an acceptable level of uncertainty.

Soil Security: Solving the Global Soil Crisis

Soil degradation is a critical and growing global problem. As the world population increases, pressure on soil also increases and the natural capital of soil faces continuing decline. International policy makers have recognized this and a range of initiatives to address it have emerged over recent years. However, a gap remains between what the science tells us about soil and its role in underpinning ecological and human sustainable development, and existing policy instruments for sustainable development. Functioning soil is necessary for ecosystem service delivery, climate change abatement, food and fiber production and fresh water storage. Yet key policy instruments and initiatives for sustainable development have under-recognized the role of soil in addressing major challenges including food and water security, biodiversity loss, climate change and energy sustainability. Soil science has not been sufficiently translated to policy for sustainable development. Two underlying reasons for this are explored and the new concept of soil security is proposed to bridge the science–policy divide. Soil security is explored as a conceptual framework that could be used as the basis for a soil policy framework with soil carbon as an exemplar indicator.

Spectroscopic models of soil organic carbon in Florida, USA.

Soil organic carbon (SOC) is an indicator of ecosystem quality and plays a major role in the biogeochemical cycles of major nutrients and water. Shortcomings exist to estimate SOC across large regions using rapid and cheap soil sensing approaches. Our objective was to estimate SOC in 7120 mineral and organic soil horizons in Florida using visible/near-infrared diffuse reflectance spectroscopy (VNIRS) calibrated by committee trees and partial least squares regression (PLSR). The derived VNIRS models were validated using independent datasets and explained up to 71 and 38% of the variance of SOC in mineral and organic horizons, respectively. We stratified the mineral horizons into seven soil orders and derived PLSR models for each order, which explained from 32% (Histosols) to 75% (Ultisols) of the variance of SOC concentration in validation mode. Estimates of SOC from all models were highly scattered along the regression lines, especially for high SOC values, and the slopes of the regression lines were generally <1 because VNIRS models tended to underestimate high SOC values and overestimate low SOC. Despite the great scatter of estimates in the prediction plots, VNIRS models had reasonable explanatory power for mineral horizons, given the heterogeneity of soils and environmental conditions in Florida, and have potential for the rapid assessment of SOC, with implications for regional SOC assessments, modeling, and monitoring. However, VNIRS models for organic horizons were hampered by small sample size and had very limited explanatory power.

Interaction effects of climate and land use/land cover change on soil organic carbon sequestration

Historically, Florida soils stored the largest amount of soil organic carbon (SOC) among the conterminous U.S. states (2.26 Pg). This region experienced rapid land use/land cover (LULC) shifts and climate change in the past decades. The effects of these changes on SOC sequestration are unknown. The objectives of this study were to 1) investigate the change in SOC stocks in Florida to determine if soils have acted as a net sink or net source for carbon (C) over the past four decades and 2) identify the concomitant effects of LULC, LULC change, and climate on the SOC change. A total of 1080 sites were sampled in the topsoil (0-20 cm) between 2008 and 2009 representing the current SOC stocks, 194 of which were selected to collocate with historical sites (n = 1251) from the Florida Soil Characterization Database (1965-1996) for direct comparison. Results show that SOC stocks significantly differed among LULC classes--sugarcane and wetland contained the highest SOC, followed by improved pasture, urban, mesic upland forest, rangeland, and pineland while crop, citrus and xeric upland forest remained the lowest. The surface 20 cm soils acted as a net sink for C with the median SOC significantly increasing from 2.69 to 3.40 kg m(-2) over the past decades. The SOC sequestration rate was LULC dependent and controlled by climate factors interacting with LULC. Higher temperature tended to accelerate SOC accumulation, while higher precipitation reduced the SOC sequestration rate. Land use/land cover change observed over the past four decades also favored the C sequestration in soils due to the increase in the C-rich wetland area by ~140% and decrease in the C-poor agricultural area by ~20%. Soils are likely to provide a substantial soil C sink considering the climate and LULC projections for this region.

CHARACTERIZATION OF THE SPATIAL DISTRIBUTION OF SOIL PROPERTIES IN WATER CONSERVATION AREA 2A, EVERGLADES, FLORIDA

Wetland soils are heterogenous in nature, and biogeochemical properties show different spatial autocorrelation structures that translate into fine- and coarse-scale spatial patterns. Understanding these patterns and how they relate to other ecosystem properties (e.g., vegetation) is critical to restore wetlands impacted by nutrient influx. Our goal was to investigate Water Conservation Area 2A, a wetland in the Florida Everglades, that has been impacted by nutrient influx and incursions of cattail as well as biogeochemical cycling of nutrients, hydrologic manipulation, and natural events (fire, hurricanes, and tropical storms). The objective of this study was to characterize the spatial patterns of soil and floc/detritus total phosphorus (TP), total inorganic phosphorus (TPi), bulk density (BD), total nitrogen (TN), total calcium (TCa), total carbon (TC), and floc depth in Water Conservation Area 2A. A total of 111 sites were sampled at three different depths (floc, 0- to 10-cm, and 10- to 20-cm depth). Geostatistical techniques were used to estimate and map soil properties across the wetland. Observed TP ranged from 155 to 1702 mg kg−1 (0-10 cm) with a mean of 551 mg kg−1 and showed strong spatial autocorrelation extending over long distances of 6864 m (10-20 cm) and 9669 m (floc). The nugget-to-sill ratio was less than 25% for all observed properties except for TN, indicating strong spatial dependence. This spatially explicit study provided insight into the variability of soil properties generated by external and internal factors and establishes a baseline framework for future management decisions involving the restoration of this wetland.

Long-term water quality trends after implementing best management practices in South Florida.

A mandatory best management practices (BMP) program was implemented in the Everglades Agricultural Area (EAA) farms basin-wide in 1995 as required by the Everglades Forever Act to reduce P loads in drainage water reaching the Everglades ecosystem. All farms in the EAA basin implement similar BMPs, and basin wide P load reductions have exceeded the 25% reduction required by law; however, differences remain in water quality between subbasins. Our objective was to determine long-term trends in P loads in discharge water in the EAA after implementing BMPs for 7 to10 yr and to explore reasons for differences in the performance of the subbasins. Two monitoring datasets were used, one from 10 research farms and the second from the EAA basin inflow and outflow locations. Mann-Kendall trend analysis was used to determine the degree of change in water quality trends. A decreasing trend in P loads was observed in general on sugarcane (Saccharum officinarum L.) farms, while mixed crop farms showed either decreasing or insignificant trends. The insignificant trends are probably related to management practices of mixed crop systems. Decreasing trends in P loads were observed in the outflow of the EAA basin, S5A, and S8 subbasins from 1992 to 2002. Inflow water from Lake Okeechobee had increasing P concentration from 1992 to 2006 with the highest trend in the east side of the lake. This analysis indicated there may be other factors impacting the success of BMPs in individual farms including cropping rotations and flooding of organic soils. Elevated P concentrations in Lake Okeechobee water used for irrigation may pose a future risk to degrade water quality on farms in the EAA, especially in the S5A subbasin.

Fit-for-purpose analysis of uncertainty using split-sampling evaluations

Abstract An uncertainty assessment method for evaluating models, the Sources of UNcertainty GLobal Assessment using Split SamplES (SUNGLASSES), is presented, which assesses predictive uncertainty that is not captured by parameter or other input uncertainties. The method uses the split sample approach to generate a quantitative estimate of the fit-for-purpose of the model, thus focusing on the purpose for which the model is used. It operates by comparing the output to be used for decision making to its observed counterpart and the associated uncertainty. The described method is applied on a Soil Water Assessment Tool (SWAT) model of Honey Creek, a tributary of the Sandusky catchment in Ohio, USA. Water flow and sediment loads are analysed. In this case study the uncertainty estimated by the proposed method is much larger than the typically estimated parameter uncertainty.

Tree-based modeling of complex interactions of phosphorus loadings and environmental factors

Phosphorus (P) enrichment has been observed in the historic oligotrophic Greater Everglades in Florida mainly due to P influx from upstream, agriculturally dominated, low relief drainage basins of the Everglades Agricultural Area (EAA). Our specific objectives were to: (1) investigate relationships between various environmental factors and P loads in 10 farm basins within the EAA, (2) identify those environmental factors that impart major effects on P loads using three different tree-based modeling approaches, and (3) evaluate predictive models to assess P loads. We assembled thirteen environmental variable sets for all 10 sub-basins characterizing water level management, cropping practices, soils, hydrology, and farm-specific properties. Drainage flow and P concentrations were measured at each sub-basin outlet from 1992-2002 and aggregated to derive monthly P loads. We used three different tree-based models including single regression trees (ST), committee trees in Bagging (CTb) and ARCing (CTa) modes and ten-fold cross-validation to test prediction performances. The monthly P loads (MPL) during the monitoring period showed a maximum of 2528 kg (mean: 103 kg) and maximum monthly unit area P loads (UAL) of 4.88 kg P ha(-1) (mean: 0.16 kg P ha(-1)). Our results suggest that hydrologic/water management properties are the major controlling variables to predict MPL and UAL in the EAA. Tree-based modeling was successful in identifying relationships between P loads and environmental predictor variables on 10 farms in the EAA indicated by high R(2) (>0.80) and low prediction errors. Committee trees in ARCing mode generated the best performing models to predict P loads and P loads per unit area. Tree-based models had the ability to analyze complex, non-linear relationships between P loads and multiple variables describing hydrologic/water management, cropping practices, soil and farm-specific properties within the EAA.

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).

Soil Phosphorus and Nitrogen Predictions Across Spatial Escalating Scales in an Aquatic Ecosystem Using Remote Sensing Images

The incorporation of remote sensing (RS) data into digital soil models has shown success to improve soil predictions. However, the effects of multiresolution imagery on modeling of biogeochemical soil properties in aquatic ecosystems are still poorly understood. The objectives of this study were the following: 1) to develop prediction models for soil total phosphorus (TP) and total nitrogen (TN) utilizing RS images and environmental ancillary data at three different resolutions; 2) to identify controlling factors of the spatial distribution of soil TP and TN; and 3) to elucidate the effects of different spatial resolutions of RS images on inferential modeling. Soil cores were collected (n = 108) from the top 10 cm in a subtropical wetland: Water Conservation Area-2A, the Florida Everglades, USA. The spectral data and derived indices from RS images, which have different spatial resolutions, included the following: MODIS (500 m resampled to 250 m), Landsat ETM+ (30 m), and SPOT (10 m). Block kriging and random forest (RF) were employed to predict soil TP and TN using RS-image-derived spectral input variables, environmental ancillary data, and soil observations. The RF models showed R2 between 0.90 and 0.93 and root mean square error between 100.4 and 115.9 mg · kg-1 for TP and between 1.45 and 1.52 g · kg-1 for TN. Soil TP was mainly predicted from RS-derived spectral indices that infer on biotic/vegetation characteristics, whereas soil TN was predicted using a combination of biotic/vegetation, topographic, and hydrologic variables. Results suggest that the spectral data informed soil models have excellent predictive capabilities in this aquatic ecosystem. Interestingly, there was no noticeable distinction among different spatial resolutions of RS images to develop prediction models for soil TP and TN in terms of error assessment. However, the variability and complexity of soil TP and TN variations were much better expressed with the finer resolution RFSPOT model than the coarser resolution RFMODIS model as demonstrated using entropy.