Actual Water Depth Research Articles

Tidal Analysis and Implementasion of an Internet of Things (IoT) Sea Level Monitoring Device in Coastal Region

Monitoring the sea level is crucial for the protection of coastal communities and infrastructure. Instruments that can record and transmit the sea level in real time are essential for preventing potential disasters. This study presents the design, construction, and evaluation of an instrument for measuring sea level using a pressure sensor, a microcontroller, and a GSM module. The sea level analyzed using T Tide analysis. The instrument's accuracy was established through a calibration process, resulting in a sensor reading accuracy of 96.76% and a low root mean square error of 3.24 cm. The linear regression model confirmed the strong correlation between sensor readings and actual water depth, with a coefficient of determination of 0.999. The instrument achieved an accuracy of 96.76% and a low root mean square error of 3.24 cm. Field testing over three days showed the instrument's reliability in measuring sea levels, with an accuracy rate of 91.93% and an root mean square error of 8.07 cm with a packet loss of 7.86%. The study area had mixed semidiurnal characteristics, with water levels ranging from 60.1 cm to 209.55 cm. Significant constituents such as principal lunar diurnal constituent (K1) and Principal lunar semidiurnal (M2) dominate the tidal patterns, each with distinct frequencies, amplitudes, and signal-to-noise ratios. This research provides a precise and cost-effective instrument for measuring water depth, which is suitable for coastal management, environmental monitoring, and scientific investigation.

River cross-section measurement using unreviewed aerial vehicle with an improved bathymetry instrument

Accurate river cross-section (RCS) data is critical for hydrological research and applications; however, in most rivers, hydrographic stations are extremely limited and RCS data are scarce. A new method for mobile measurements of RCS is herein proposed based on an unreviewed aerial vehicle (UAV) platform, i.e., using UAV aerial survey to obtain above-water cross-sections and a self-made UAV bathymetric instrument to attain underwater cross-sections. Underwater cross-sectional measurement is the focus of this paper, namely UAV bathymetry (UAVB). The effectiveness of this method has been verified in rivers with different widths, water depths, turbidity, and flow velocities. The results show that the UAVB cross-section is consistent with the underwater cross-section measured in the field via conventional surveying. The surface velocity is the main factor affecting the accuracy of UAVB, and the corresponding measurement accuracy is ～4%, ～8%, and ～18% of the actual water depth when the surface velocity is less than 1 m/s, 1–2 m/s, and greater than 2 m/s, respectively. From the combination of hydraulic parameters obtained by UAV aerial survey and the calculated discharge results of three types of RCSs (measured cross-section, UAVB cross-section, and generalized cross-section), our results indicate that discharge calculated from the measured cross-section has the highest accuracy with a mean relative error (MRE) of ～5%, followed by the UAVB cross-section with MRE of ～15%, and the generalized cross-section has the worst accuracy with MRE ～35%.

Automatic detection of actual water depth of urban floods from social media images

Urban flooding disasters are the most frequent natural disasters in cities, which broadly affects people's livelihood. Prompt emergency response is the key to alleviating such impact, however, that would rely on the timely mapping of flooding according to waterlogging severity. Compared with traditional approaches, crowdsourcing images from social media has emerged as a more efficient way to obtain this type of information. Therefore, this paper aims to explore the approach based on computer vision technology to automatically extract water depth from social media images, which can be utilized to construct flooding maps during urban flooding. Firstly, the images related to urban flooding were retrieved from social media and then filtered, leaving only images containing people. Secondly, a specific objective detection model based on YOLO was trained to detect the human body parts which are divided into crus, thigh, shoulder, and head from bottom to top. The experiment shows that the mAP of the trained human body parts detection model in the test dataset is 0.967. Afterward, an algorithm for extracting water depth was proposed based on the ratio of bounding boxes of detected body parts in images to the actual length of human body parts. Next, these models were verified by comparing them with manually estimated depth range and manually measured depth. The experiment shows that the accuracy of water depth range is 0.959 and the mean absolute error of the actual depth detection of water is 10.22 cm in the measurement dataset. Finally, the proposed models were applied to map the 2016 flooding that occurred in Wuhan as an illustrative case. This approach is helpful to broaden the source of flooding severity information and improve the efficiency of flood mapping in urban flood emergency management.

Automatic Detection of Actual Water Depth of Urban Floods from Social Media Images

Automatic Detection of Actual Water Depth of Urban Floods from Social Media Images

Evaluation of Water Delivery Performance and Conveyance Efficiency in Gatto Small-Scale Irrigation Scheme, Southern, Ethiopia

Irrigation could serve as a viable solution to address the problem of food insecurity by increasing, boosting agricultural production. This research work was conducted to evaluate the performance of Gatto irrigation scheme using some selected performance indicators specifically, water delivery performance, conveyance efficiency and maintenance indicators of the scheme with in the investigation period of October to December 2017. The discharge measurement was performed at twelve measuring points on main canal and eight at two secondary cannels by using VALEPORT automatic current meter and 3-inch Parshall flumes for each selected nine tertiary offtakes from three each at the head, middle and tail end of the scheme. Water delivery performance evaluated with respect to adequacy, equity, efficiency and dependability was focused on variability in discharges of water distribution from head to tail reach and, during the crop season from October to December with overall average value equal to 0.70, 0.44, 0.89 and 0.53 respectively. Thus, the irrigation scheme when compared with Molden and Gates (1990) standards was found under poor condition in adequacy, equity and dependability and under good condition in efficiency. Estimated overall water conveyance efficiencies of the main, first and secondary canals were 86.3%, 77.53% and 73.13% respectively. However, the result found in the scheme was smaller than FAO (1989b) recommended value (95%) and (90%) for lined and unlined cannels with clay soil type. The design depth of water based on the design document was 0.42m nevertheless the actual depth of water in the main canal was 0.26 m due to this the relative change of the water level was 38.1% this greater than 0 value indicates that the intended water level in the main canal has not been achieved due to sediment accumulation in the canal. The result of Gatto small scale irrigation scheme of effective of infrastructures is reckoned to be 57.2 percent and sustainability of irrigation scheme is 88%. Many of the structures are demolished and became dysfunctional because of scouring, sedimentation and silt accumulation. Generally, the performance of the system is poor, it mainly results due to inadequate operation and maintenance provisions and over abstraction of water by upstream users. Therefore, adequate maintenance and other suitable management and operation approaches are required to improve the irrigation system performance. So, all stakeholders must work together in order to minimize factors that hinder the good performance of the irrigation system.

Detecting Water Depth from Remotely Sensed Imagery Based on ELM and GA-ELM

The shallow seawater depth inversion based on remote sensing technology is important for water depth detection, which is of considerable significance to marine engineering, shipping, and marine military security. In this study, we took the Taiping Island and its adjacent waters in the South China Sea as a test bed and developed a water depth inversion model on the basis of extreme learning machine (ELM) and extreme learning machine optimized by genetic algorithm (GA-ELM). In GA-ELM, the input weights and the hidden layer biases were optimized by genetic algorithm. The two models allowed the evaluation of nonlinear relationships between the reflectance of high-resolution imagery from WorldView-2 and actual water depth obtained from the S-57 sea chart. The eight bands of the high-resolution image and the actual water depth were used as the input layer and the output layer, and the sigmoid function was introduced as activation function. Finally, the model accuracy was evaluated by using mean relative error (MRE), root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and the regression analysis between the retrieved water depth and the actual data. The simulation results showed that the two models had better stability than the second-order polynomial regression, BP neural network, and RBF neural network. Furthermore, GA-ELM had a more compact network structure and better generalization ability than ELM. Thus, we concluded that GA-ELM had higher precision and could achieve a better inversion result in the experimental area.

Determination of sediment deposition of Hasanlar Dam using bathymetric and remote sensing studies

Hasanlar Dam and Hydroelectric Power Plant are located on Kucuk Melen Creek in the Western Black Sea Basin of Turkey. The dam was constructed in 1974 to provide domestic water needs of the Duzce Province, to supply irrigation water need, to control and mitigate floods and to produce hydroelectric power. This dam has been subjected to severe sedimentation since its construction in 1974. Therefore, bathymetric field survey studies were conducted to determine storage loss in the Hasanlar Dam reservoir by sedimentation. Bathymetric survey data from the reservoir site of the Hasanlar Dam were obtained in 1979, 1999 and 2014. Analysis of the bathymetric data, GIS and remotes sensing techniques showed that storage loss in reservoir active volume between 1974 and 1999 was 24% and between 1974 and 2014 storage loss was 26%. Analysis of the bathymetric maps also showed that sediment accumulation is severe near and around the dam body and the spillway whose discharge capacity was decreased by sediment accumulation. This is extremely critical because the flood of May 1998 caused the high risk of collapse of dam due to reduced capacity of the spillway. Remote sensing technique was used to determine the future deposition of sediment in the reservoir. For this purpose, 35 points in the reservoir area were determined by comparing the relative water depths and actual water depths using satellite image of the bathymetry in July 2017 and Lake Observation Station. High correlation (R2 = 0.833) was calculated by using logarithmic nonlinear regression analysis between actual and relative water depths for those 35 control points. The average of absolute values of differences between the estimated and actual water depths was found as 1.06 m, and RMSE was calculated as 1.25 m. This analysis shows that in the future, remote sensing data can be used in the studies of determining the depth of water and the total sediment thickness. In addition, the volume of the entire reservoir can be predicted by measuring the actual water depth only at those 35 control points without making a bathymetric map of the whole dam reservoir.

The limited link between accommodation space, sediment thickness, and inner platform facies distribution (Holocene–Pleistocene, Bahamas)

AbstractCyclic facies variations in shallow‐water carbonate platforms often show repetitive facies patterns that are frequently interpreted to reflect the sedimentary response to variations in sea‐level related to changes in climate linked to orbital variations, the Milankovitch frequencies. Whether these shallow‐marine carbonates represent a complete infill of accommodation space, or are subtidal cycles, has been discussed in numerous papers. The extent to which the thickness of a single depositional cycle is a direct measure of the amplitude of relative sea‐level change is not fully understood. New shallow seismic data from Great Bahama Bank reveal that accommodation space created during the Holocene sea‐level rise is not filled in a predictable way. Three seismic horizons were identified: the seabed, the Pleistocene top, and a horizon within the Pleistocene. Depth surface and thickness maps of the Holocene and Pleistocene layers were combined with 326 in situ water‐depth measurements to assess the upper limit of the present accommodation space. The analysis showed that accommodation space and Holocene sediment thickness, and water depth are not correlated. In addition, the actual water depth and inner platform facies distribution showed no straightforward link. The energy distribution across the shallow‐water platform appears to control the facies type rather than water depth. Mud‐dominated sediments prevail in shallow low‐energy areas protected by a topographic barrier, whereas mud‐free coarse‐grained sediments mainly occur in deeper areas with hydrodynamic energy induced by strong tidal currents, ocean water influx, and winds. Hence, the uneven energy distribution not only results in unpredictable differences in the carbonate‐cycle thickness on the platform but also to a water depth independent facies distribution pattern within the inner platform. Therefore, care should be taken when deducing sea‐level signals from inner platform facies distribution and sediment thickness patterns on ancient platforms.

Improving the remediation capacity of a landfill leachate channel by selecting suitable macrophytes

To assess the remediation capacity of a leachate channel, we monitored basic environmental parameters such as bathymetry, leachate, and soil characteristics and vegetation coverage. Based on our results, we designed a series of experiments to determine the most suitable remediating plant species for sustainable wastewater treatment. We found that adaptability to water depth may be a prime driver of reduced remediation capacity. Large portions of the leachate channel were deeper than the maximum tolerance range of many candidate emergent macrophytes, resulting in only 16% total vegetation coverage. Among tested species, Typha angustifolia showed the most promising potential for remediation, reaching the highest aboveground biomass (3300 g/m2) and demonstrating maximum concentrations in tissues (34,600 mg/kg of Na, 4013 mg/kg of Mg, 904 mg/kg of P, 639 mg/kg of Mn, 191 mg/kg of Fe and 62 mg/kg of Zn) when grown in leachate filled tank for six months. Typha angustifolia also showed greater tolerance of water depth than Phragmites australis, which previously was planted in leachate channels. Thus, T. angustifolia should be more suitable for the actual water depth of the channel. Additional planting of T. angustifolia will improve the vegetation coverage, the total remediation capacity and sustainability of the leachate channel. Considering water depths of target wetlands when selecting remediation plant will improve remediation ability and sustainability of remediation wetlands.

Unmanned aerial vehicle observations of water surface elevation and bathymetry in the cenotes and lagoons of the Yucatan Peninsula, Mexico

Observations of water surface elevation (WSE) and bathymetry of the lagoons and cenotes of the Yucatan Peninsula (YP) in southeast Mexico are of hydrogeological interest. Observations of WSE (orthometric water height above mean sea level, amsl) are required to inform hydrological models, to estimate hydraulic gradients and groundwater flow directions. Measurements of bathymetry and water depth (elevation of the water surface above the bed of the water body) improve current knowledge on how lagoons and cenotes connect through the complicated submerged cave systems and the diffuse flow in the rock matrix. A novel approach is described that uses unmanned aerial vehicles (UAVs) to monitor WSE and bathymetry of the inland water bodies on the YP. UAV-borne WSE observations were retrieved using a radar and a global navigation satellite system on-board a multi-copter platform. Water depth was measured using a tethered floating sonar controlled by the UAV. This sonar provides depth measurements also in deep and turbid water. Bathymetry (wet-bed elevation amsl) can be computed by subtracting water depth from WSE. Accuracy of the WSE measurements is better than 5–7 cm and accuracy of the water depth measurements is estimated to be ~3.8% of the actual water depth. The technology provided accurate measurements of WSE and bathymetry in both wetlands (lagoons) and cenotes. UAV-borne technology is shown to be a more flexible and lower cost alternative to manned aircrafts. UAVs allow monitoring of remote areas located in the jungle of the YP, which are difficult to access by human operators.

Mining Channel Water Depth Information From IoT-Based Big Automated Identification System Data for Safe Waterway Navigation

Internet of Things technology has been widely used in water traffic research. Many critical waterways in the world are becoming more crowded due to many factors in waterway environments, such as invisibility, variability, and uncertainty. Accurate water depth information is necessary to improve navigation safety. Water depth information of electronic charts cannot be updated in a timely way, while the actual water depth is unpredictable, and this factor threatens the safety of vessels in waterway environments. Based on the shore-based network, ship navigation data and other big data can be integrated to vessels navigation environments in real time. In this paper, we present a new scheme to quickly and accurately construct a vessel safety navigation depth reference map, which contains appropriate channel water depth information. This effective scheme is based on automated identification system (AIS) data and increases the travel safety through crowded waterways. AIS data include rich maritime traffic information. Both the static and the dynamic information about vessels through waterways can be extracted and processed from big real-time AIS data. Based on extensive actual experiments, we apply data mining techniques to extract the waterway depth information both draft-depth and vessel trajectories based on AIS data. The data are collected from vessels in both locations: 1) the Nantong port, in Jiangsu Province, China and 2) Meizhou Bay waterway, in Fujian Province, China. The Hermite interpolation scheme is used to patch the trajectories of vessels, and the BP neural network model is introduced to predict the maximum vessel draft. Clustering and data fusion methods are employed to construct a vessel safety navigation depth reference map according to the cluster area of vessel trajectories and draft information. The experimental results demonstrate that the vessel safety navigation depth reference map accurately reflects the current water depth profile of channels. This paper can provide accurate and timely channel water-depth information for the vessel navigation and the maritime supervision. The proposed scheme in this paper can also provide reference for trajectory data processing and mining.

Depth classification of underwater targets based on complex acoustic intensity of normal modes

In order to solve the problem of depth classification of the underwater target in a very low frequency acoustic field, the active component of cross spectra of particle pressure and horizontal velocity (ACCSPPHV) is adopted to distinguish the surface vessel and the underwater target. According to the effective depth of a Pekeris waveguide, the placing depth forecasting equations of passive vertical double vector hydrophones are proposed. Numerical examples show that when the sum of depths of two hydrophones is the effective depth, the sign distribution of ACCSPPHV has nothing to do with horizontal distance; in addition, the sum of the first critical surface and the second critical surface is equal to the effective depth. By setting the first critical surface less than the difference between the effective water depth and the actual water depth, that is, the second critical surface is greater than the actual depth, the three positive and negative regions of the whole ocean volume are equivalent to two positive and negative regions and therefore the depth classification of the underwater target is obtained. Besides, when the 20 m water depth is taken as the first critical surface in the simulation of underwater targets (40 Hz, 50 Hz, and 60 Hz respectively), the effectiveness of the algorithm and the correctness of relevant conclusions are verified, and the analysis of the corresponding forecasting performance is conducted.

Spatially explicit feedbacks between seagrass meadow structure, sediment and light: Habitat suitability for seagrass growth

In shallow coastal bays where nutrient loading and riverine inputs are low, turbidity, and the consequent light environment are controlled by resuspension of bed sediments due to wind-waves and tidal currents. High sediment resuspension and low light environments can limit benthic primary productivity; however, both currents and waves are affected by the presence of benthic plants such as seagrass. This feedback between the presence of benthic primary producers such as seagrass and the consequent light environment has been predicted to induce bistable dynamics locally. However, these vegetated areas influence a larger area than they footprint, including a barren adjacent downstream area which exhibits reduced shear stresses. Here we explore through modeling how the patchy structure of seagrass meadows on a landscape may affect sediment resuspension and the consequent light environment due to the presence of this sheltered region. Heterogeneous vegetation covers comprising a mosaic of randomly distributed patches were generated to investigate the effect of patch modified hydrodynamics. Actual cover of vegetation on the landscape was used to facilitate comparisons across landscape realizations. Hourly wave and current shear stresses on the landscape along with suspended sediment concentration and light attenuation characteristics were then calculated and spatially averaged to examine how actual cover and mean water depth affect the bulk sediment and light environment. The results indicate that an effective cover, which incorporates the sheltering area, has important controls on the distributions of shear stress, suspended sediment, light environment, and consequent seagrass habitat suitability. Interestingly, an optimal habitat occurs within a depth range where, if actual cover is reduced past some threshold, the bulk light environment would no longer favor seagrass growth.

Lake bathymetry from Indian Remote Sensing (P6‐LISS III) satellite imagery using artificial neural network model

AbstractThe remote sensing technique provides a rapid and relatively inexpensive means of identifying silted areas in large water bodies, in order that desilting activities can be effectively conducted. This study developed lake bathymetry for a selected lake system (Akkulam–Veli Lake, Kerala, India) from the Indian Remote Sensing (IRS P6‐LISS III) satellite imagery, using an artificial neural network (ANN) model. The water depth was measured for 17 months at different points in the lake on the same date of overpass of the IRS satellite. The satellite imageries obtained for 12 December 2007 and 16 February 2009 were identified as cloud‐free images. ANN models were developed with the four input series of radiance values from green, red, NIR and MIR bands observed for the satellite imagery obtained on 12 December 2007 at the sampling sites, with actual water depth measurements also being taken on the same date. A three‐layered feed forward neural network with back propagation training algorithm was developed for this study. To train the model, it was run several times by changing the number of neurons, learning rate and the momentum constants until the mean square error was minimum. When the number of neurons is increased to 35, and the logsig function is used as ANN transfer function, the error becomes minimum. To test the model, the developed ANN was run for a new set of input from the satellite imagery taken on 16 February 2009. Comparing the predicted and measured values for the same sites for the same day, it was found that the model is best suited for predicting water depth using ANN and the radiance values for four bands of IRS satellite imagery. The results of this study indicated that, for the shallow lake with lower depth, the difference between the actual and predicted value was considerable. In contrast, this was not the case where the lake water depth was greater, indicating an increased prediction accuracy with ANN with increasing depths for shallow lakes. A bathymetry map prepared with ANN indicated only the lake shoreline, as well as the shallow littoral zones. The approach used in this study requires further refinement, including further of the model based on using more field measurements to obtain a better bathymetry map.

The time evolution of scour around offshore structures

This paper describes the development of an engineering model to predict the development of scour evolution through time around an offshore structure under current, wave and combined wave–current flows. The model has been tested against a range of data as well as being run for idealised tests. From the results of these tests issues with respect to scour prediction have been highlighted. In particular, the initial growth rate of scour appears to be too rapid based on the large-scale laboratory tests and tidal field data against which the model has been compared. Application of the model to a field-scale study has been completed which shows that in shallow water depths storm waves dominate the scour process. In deeper water depths, currents generally dominate even under storm conditions, although this will be dependent on the wave height and period and the actual water depth. The scour time evolution predictor (Step) model can be applied to scour assessment studies where there is a requirement to have an indication of the time-history of scour. The model clearly has a role in offshore wind turbine studies, but it is not limited to this sector and can be applied to bridge scour situations or other situations where scouring around monopile structures is an issue. Furthermore, if the scour at other types of structures or objects needs to be evaluated then provided the equilibrium scour depth and time-scale functions are available these can be implemented within the framework of the Step model.

Effects of Tidal and Diel Cycles on Dugong Habitat Use

ABSTRACT Quantifying the factors influencing behaviors of aquatic mammalian grazers may enhance the generic understanding of grazer ecology. We investigated diel and tidal patterns in movements of the dugong (Dugong dugon) by Global Positioning System—tracking 12 animals in 5 inshore—intertidal and 3 offshore—subtidal habitats along the coast of Queensland, Australia. We examined effects of tide height and time of day on the dugong's distance from 1) the nearest coast, 2) water >3 m deep, 3) actual water depth (bathymetry + tide ht) experienced, and 4) distribution of the directions of movements. Both tidal and diel cycles influenced dugong movement. Tracked dugongs tended to be closer to shore at high tide than at low tide and closer to shore at night than during the day. Onshore movement was more prevalent on incoming tides and in the afternoon and evening. Offshore movement was more prevalent on outgoing tides and from midnight through the morning until midday. Tidal and diel variation in water depths used by the inshore—intertidal dugongs was small, but probably underestimated, hidden by a sampling bias in the telemetry equipment. Onshore movement at high tide allowed dugongs to exploit intertidal seagrass beds. Dugongs are closer to shore in afternoons and evenings than in mornings. This behavior may be related to the avoidance of predators or watercraft. Our findings can be used to predict spatial patterns of dugongs within areas of conservation management significance and to assess, avoid, and mitigate adverse effects of anthropogenic disturbance.

Mass conserving modelling of aquatic ecosystems with a variable tide level—Why extrapolation from point tracers is inadmissible and how to solve the problem with a semi-Lagrangian approach

In earlier Lagrangian descriptions of aquatic systems point tracers were considered for describing the pelagic part of the systems. A coupling of such a model to a benthic model is not satisfactory if the system’s water level is variable and mass conservation is needed. The article shows that a pelagic–benthic coupling is not possible by extrapolating the properties of the tracers to the total water body. In the second part a solution for this problem is given. In this concept the tracers are assumed to have a finite volume and the total volume of all tracers within a considered region at a considered time represents the actual volume of the real system. The coupling to the benthic system is realized by a semi-Lagrangian scheme defining a contact area of every tracer to the sediment. The magnitude of the contact area depends on the actual water depth. The exchange processes between the pelagic and the benthic system are calculated for every contact area.

Comparação da evapotranspiração real simulada e observada em uma bacia hidrográfica em condições naturais de cerrado

Através deste trabalho, objetivou-se: (i) simular a evapotranspiração real de uma microbacia sob vegetação natural de cerrado, ao longo de um ano, compreendido entre os dias 04/08/98 e 03/08/99; (ii) comparar e analisar o comportamento da evapotranspiração real, do escoamento total e da lâmina de água armazenada no solo, através dos valores medidos e simulados. Utilizando-se a equação do balanço hídrico, determinou-se a evapotranspiração real total do período (830,53 mm) ao longo do qual foram efetuadas medições intermediárias que permitiram a realização do balanço hídrico em 21 intervalos de tempo. Os valores simulados de evapotranspiração real e de escoamento total ao longo do período analisado não apresentaram boa correlação com os dados obtidos experimentalmente; entretanto, os valores simulados da lâmina atual de água no solo até um metro de profundidade, apresentaram alta correlação com os valores experimentais. Observou-se, no período mais seco do ano, que, apesar da alta evapotranspiração de referência, a evapotranspiração real apresentou-se muito pequena, devido à falta de água disponível para evapotranspirar.

A County‐Level Assessment of Ground Water Contamination by Pesticides

AbstractA pesticide screening model was integrated with a geographic information system (GIS) for evaluating the ground water vulnerability to pesticide contamination in Albemarle County, Virginia. The attenuation factor (AF). an index of pesticide mass emission from the vadose zone, was used to evaluate the relative contamination potential of 70 pesticides used in the county. Results for only three pesticides—atrazine, dicamba, and lindane—are discussed in this paper. Spatial (land use, soils, and hydrogeology) and relational (soil and pesticide properties) data layers were combined with the AF model within the GIS environment for spatial computation of AF for actual and 2 m ground water depths. For each pesticide, a ground water vulnerability map with five contamination potential categories (high, medium, low, very low, and unlikely) was generated, based on the spatial distribution of AF for each cell size of 0.27 acre (0.11 ha). To consider the variability in pesticide transport, model simulations were performed for “maximum,”“average,” and “minimum” scenarios of pesticide leaching. Under the average leaching scenario (2 m depth), the three pesticides were found to have very low to low levels of contamination potential in some areas. For maximum leaching scenario (2 m depth), contamination potential of these three pesticides increased to low to medium levels. When actual ground water depth was used, no significant contamination potential was indicated by any of the three pesticides. The modeling approach was evaluated using the data from a limited monitoring study in Albemarle County. Although agreement between the model prediction and actual pesticide detection was observed, extent of the data was not sufficient enough to draw firm conclusions about the model's success in predicting contamination potential. A sensitivity analysis of the methodology revealed that significant uncertainty can be involved in predicting the county‐scale contamination potential. To make better use of this study, it is recommended that a comprehensive pesticide monitoring be undertaken in Albemarle County.

Mirages in shallow water matched-field processing

Broadband (50–200 Hz) matched field processing was performed on vertical line array data from a recent shallow water experiment. Although the actual water depth steadily decreased from 200 to 100 m over the source tow track, the replica vectors were calculated assuming a range-independent environment of 200 m depth. Rather than breaking up due to the increasingly severe environmental mismatch, the broadband matched field output peak in range and depth behaved in a consistent way; both the predicted range and depth of the source became increasingly greater than its true range and depth as the true water depth decreased. A simple analytical model was developed to predict the behavior of these MFP mirages. The good agreement between model predictions and actual results suggests that the model can be used to quantify the impact of unknown water depth changes (e.g., ocean swell on synthetic aperture studies) and to ‘‘calibrate’’ water depth in a region using the sinking mirages of surface ships of opportunity. By use of the concept of ‘‘effective depth’’ developed by D. E. Weston et al., the model also can be applied to studies of ocean bottom geoacoustic parameter mismatch. [Work supported by ONR, code 321.]