Assessment of soil factors controlling ephemeral gully erosion on agricultural fields

The soil factor is crucial in controlling and properly modeling the initiation and development of ephemeral gullies (EGs). Usually, EG initiation has been related to various soil properties (i.e. sealing, critical shear stress, moisture, texture, etc.); meanwhile, the total growth of each EG (erosion rate) has been linked with proper soil erodibility. But, despite the studies to determine the influence of soil erodibility on (ephemeral) gully erosion, a universal approach is still lacking. This is due to the complex relationship and interactions between soil properties and the erosive process. A feasible soil characterization of EG erosion prediction on a large scale should be based on simple, quick and inexpensive tests to perform. The objective of this study was to identify and assess the soil properties – easily and quickly to determine – which best reflect soil erodibility on EG erosion. Forty‐nine different physical–chemical soil properties that may participate in establishing soil erodibility were determined on agricultural soils affected by the formation of EGs in Spain and Italy. Experiments were conducted in the laboratory and in the field (in the vicinity of the erosion paths). Because of its importance in controlling EG erosion, five variables related to antecedent moisture prior to the event that generated the gullies and two properties related to landscape topography were obtained for each situation. The most relevant variables were detected using multivariate analysis. The results defined 13 key variables: water content before the initiation of EGs, organic matter content, cation exchange capacity, relative sealing index, two granulometric and organic matter indices, seal permeability, aggregates stability (three index), crust penetration resistance, shear strength and an erodibility index obtained from the Jet Test erosion apparatus. The latter is proposed as a useful technique to evaluate and predict soil loss caused by EG erosion. Copyright © 2018 John Wiley & Sons, Ltd.     

A method for uncertainty constraint of catchment discharge and phosphorus load estimates

River discharge and nutrient measurements are subject to aleatory and epistemic uncertainties. In this study, we present a novel method for estimating these uncertainties in colocated discharge and phosphorus (P) measurements. The “voting point”‐based method constrains the derived stage‐discharge rating curve both on the fit to available gaugings and to the catchment water balance. This helps reduce the uncertainty beyond the range of available gaugings and during out of bank situations. In the example presented here, for the top 5% of flows, uncertainties are shown to be 139% using a traditional power law fit, compared with 40% when using our updated “voting point” method. Furthermore, the method is extended to in situ and lab analysed nutrient concentration data pairings, with lower uncertainties (81%) shown for high concentrations (top 5%) than when a traditional regression is applied (102%). Overall, for both discharge and nutrient data, the method presented goes some way to accounting for epistemic uncertainties associated with nonstationary physical characteristics of the monitoring site.     

Capture Versus Capture Zones: Clarifying Terminology Related to Sources of Water to Wells

The term capture, related to the source of water derived from wells, has been used in two distinct yet related contexts by the hydrologic community. The first is a water‐budget context, in which capture refers to decreases in the rates of groundwater outflow and (or) increases in the rates of recharge along head‐dependent boundaries of an aquifer in response to pumping. The second is a transport context, in which capture zone refers to the specific flowpaths that define the three‐dimensional, volumetric portion of a groundwater flow field that discharges to a well. A closely related issue that has become associated with the source of water to wells is streamflow depletion, which refers to the reduction in streamflow caused by pumping, and is a type of capture. Rates of capture and streamflow depletion are calculated by use of water‐budget analyses, most often with groundwater‐flow models. Transport models, particularly particle‐tracking methods, are used to determine capture zones to wells. In general, however, transport methods are not useful for quantifying actual or potential streamflow depletion or other types of capture along aquifer boundaries. To clarify the sometimes subtle differences among these terms, we describe the processes and relations among capture, capture zones, and streamflow depletion, and provide proposed terminology to distinguish among them.     

1,4‐Dioxane Soil Remediation Using Enhanced Soil Vapor Extraction (XSVE): II. Modeling

1,4‐Dioxane is a volatile organic compound that is fully miscible in water, allowing it to sequester in vadose zone pore water and serve as a long‐term source of groundwater contamination. Conventional soil vapor extraction (SVE) removes 1,4‐dioxane; however, substantial 1,4‐dioxane can remain even after other colocated chlorinated solvents have been remediated. A field demonstration of “enhanced SVE” (XSVE) with focused extraction and heated injection was conducted at former McClellan AFB, CA, achieving 94% reduction in soil concentrations. A screening‐level tool, HypeVent XSVE, was created to assist in system design and data reduction and to anticipate how operating factors affect XSVE performance (e.g., cleanup level, remediation time, etc.). It assumes well‐mixed conditions, and combines an energy balance, mass balances for water and contaminant, and a temperature‐dependent 1,4‐dioxane Henry's Law constant. User inputs include the target treatment zone size, initial 1,4‐dioxane and soil moisture concentrations, and ambient site and injection/extraction conditions (temperature, humidity). Projections based on inputs representative of demonstration site conditions adequately anticipated the observed macroscopic field results. Sensitivity analyses show that removal increases with increasing heated air injection temperature and relative humidity and decreasing initial soil moisture content.     

Regeneration and utilization of nutrients during collapse of a <Emphasis Type="Italic">Mesodinium rubrum</Emphasis> red tide and its influence on phytoplankton species composition

High-biomass red tides occur frequently in some semi-enclosed bays of Hong Kong where ambient nutrients are not high enough to support such a high phytoplankton biomass. These high-biomass red tides release massive inorganic nutrients into local waters during their collapse. We hypothesized that the massive inorganic nutrients released from the collapse of red tides would fuel growth of other phytoplankton species. This could influence phytoplankton species composition. We tested the hypothesis using a red tide event caused by Mesodinium rubrum (M. rubrum) in a semi-enclosed bay, Port Shelter. The red tide patch had a cell density as high as 5.0×10⁵ cells L^?1, and high chlorophyll a (63.71 μg L^?1). Ambient inorganic nutrients (nitrate: \(\rm{NO}_3^-\), ammonium: \(\rm{NH}_4^+\), phosphate: \(\rm{PO}_4^{3-}\), silicate: \(\rm{SiO}_4^{3-}\)) were low both in the red tide patch and the non-red-tide patch (clear waters outside the red tide patch). Nutrient addition experiments were conducted by adding all the inorganic nutrients to water samples from the two patches followed by incubation for 9 days. The results showed that the addition of inorganic nutrients did not sustain high M. rubrum cell density, which collapsed after day 1, and did not drive M. rubrum in the non-red-tide patch sample to the same high-cell density in the red tide patch sample. This confirmed that nutrients were not the driving factor for the formation of this red tide event, or for its collapse. The death of M. rubrum after day 1 released high concentrations of \(\rm{NO}_3^-\), \(\rm{PO}_4^{3-}\), \(\rm{SiO}_4^{3-}\), \(\rm{NH}_4^+\), and urea. Bacterial abundance and heterotrophic activity increased, reaching the highest on day 3 or 4, and decreased as cell density of M. rubrum declined. The released nutrients stimulated growth of diatoms, such as Chaetoceros affinis var. circinalis, Thalassiothrix frauenfeldii, and Nitzschia sp., particularly with additions of \(\rm{SiO}_4^{3-}\) treatments, and other species. These results demonstrated that initiation of M. rubrum red tides in the bay was not directly driven by nutrients. However, the massive inorganic nutrients released from the collapse of the red tide could induce a second bloom in low-ambient nutrient water, influencing phytoplankton species composition.     

Multidisciplined investigation to locate the Kentucky shipwreck

A local artifact collector contacted the U.S. Army Engineer District, Vicksburg in the fall of 1994 concerning timbers from a ship protruding from the west bank of the Red River, Louisiana, during the summer of 1994. From historical records it was determined that the shipwreck is that of the Kentucky, which sank in 1865. An investigation integrating archaeological, geological, and geophysical methods was undertaken to determine the location, orientation, and depth of the wreckage. Based on site geology, the shipwreck is expected to lie within a silty sand deposit within 4–8.5 m below the ground surface. The results of probe investigations performed by the archaeologists and borehole data orient the long axis of the ship N57.5E. This orientation conflicts with the geophysical data, which suggest an orientation of N116W. The geophysical surveys identified an anomalous region, approximately 31 m long and 10 m wide, that borders the Red River and an abandoned channel of the Red River. The magnetometer data suggests that the bow of the ship is pointed toward the abandoned channel. This orientation contradicts historical records which have the bow pointing toward the modern Red River. A subsequent underwater investigation by the archaeological contractor discovered a rudder‐related mechanism protruding from the bank of the Red River, confirming the geophysical prediction. All three scientific surveys agree that the wreckage is at a depth 3.8–8.5 m below ground surface and at an inclined position, with a downward slope from the bank of the Red River to the abandoned channel. © 2000 John Wiley & Sons, Inc.*     

A framework to facilitate development and testing of image-based river velocimetry algorithms

Image-based methods have compelling, demonstrated potential for characterizing flow fields in rivers, but algorithms like particle image velocimetry (PIV) must be further tested and improved to enable more effective use of these techniques. This paper presents a framework designed for this exact purpose: Simulating Hydraulics and Images for Velocimetry Evaluation and Refinement (SHIVER). The approach involves coupling a hydrodynamic model with a synthetic particle generator to advect particles between frames, as dictated by local velocity vectors and thus construct a plausible image sequence specific to the reach of interest. The resulting time series can then be used as input to a velocimetry algorithm to compare image-derived estimates with known (modelled) velocities to perform an exhaustive, spatially distributed accuracy assessment. As an example application of SHIVER, we examined the effects of interrogation area (IA) size, frame rate, flow velocity, and image sequence duration on the performance of a standard PIV algorithm. This analysis indicated that image-derived velocities were generally in close agreement with those from the flow model (root mean square error <10% and mean bias <3%), except when small IAs were coupled with low frame rates. Velocity estimates were most accurate for the lowest modelled discharge ( $$ at baseflow) and became less reliable as the mean flow velocity increased ( $$ for an intermediate discharge and $$ at bankfull). Accuracy was essentially independent of image sequence duration, implying that long occupations might not be necessary. Errors were concentrated along channel margins, where PIV-based velocities tended to be greater than those from the flow model. Small IAs led to underpredictions of velocity, while larger IAs led to overpredictions. SHIVER is highly modular and could be updated to make use of different hydrodynamic models or image simulators. The framework could also facilitate more thorough sensitivity analyses and comparison of various velocimetry algorithms.     

A framework for creating and validating a non-linear spectrum-biomass model to estimate the secondary succession biomass in moist tropical forests

Uncertainties remain in the use of remote sensing technologies to provide validated model-derived estimates of the biomass of the secondary succession (SS) forests in the Amazon Basin. The objectives of this study were to develop a modeling framework for creating a valid spectrum-biomass model to estimate the SS biomass, to assess the utility of the framework and the accuracy and validity of the model, and to identify the model’s determinants. Data sources for this study include 1992–1993 vegetation inventory data and 1991 Landsat Thematic Mapper (TM) data on the Altamira region of Para, Brazil, and 1994–1995 vegetation inventory data and 1994 Landsat TM data on the nearby Bragantina region. The allometric approach was used to estimate the biomass of the sampled sites based on the vegetation inventory data. A framework for the spectrum-biomass regression model was developed based on the estimated biomass of the sampled sites and the Landsat data. The framework includes (1) the pooling of data from Bragantina and the use of ANCOVA to justify this approach; (2) image calibration; (3) biomass data age-adjustment, (4) selection of independent variables, (5) regression model development, and (6) model assessment and validation. The cubic regression model with TM Band5-related predictors was found to best fit the data as evidenced by an adjusted R-squared value of 0.865, mean square error (MSE) of the model, and the analysis of residuals. Residual analysis showed that the model might yield a better estimation on a lower biomass values than on higher biomass values. In addition, further analyses identified several determinants that can impact the accuracy of the spectrum-biomass model. ANCOVA confirmed that the relationship between the biomass and the spectrum is independent of the Altamira and Bragantina regions, and that it was appropriate to pool sampled data from both regions in the proposed model. The model development methodology and the model produced from this research will be of value to researchers using the spectrum-biomass modeling approaches to estimate the biomass and study the SS rates in moist tropical forests.     

Towards Real‐Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

Geodemographic classifications provide discrete indicators of the social, economic and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end, this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.