Using exogenous variables to improve precipitation predictions of ANNs in arid and hyper-arid climates

Because of scarcity and high variability of rainfall in arid areas, from one hand, reliable prediction of precipitation in such regions is considerably difficult. Furthermore, in some cases, shortage of observation data and several other limitations may intensify complexity of the forecasting. On the other hand, these regions highly suffer from low availability of water which necessitates development of an appropriate modeling approach to provide as precise as possible predictions of precipitation. Artificial neural networks (ANNs) are expected to be a powerful tool in capturing and analyzing high interannual variability of precipitation in arid climates and, subsequently, in proper prediction of precipitation fluctuations in the future. The end of this paper is to improve ANN predictions of precipitation in arid climates using better training of the network. To this end, two approaches were applied. In the first one, just the rainfall monthly data were considered as input. In the second approach, in addition to precipitation, several exogenous variables of precipitation are considered as input to predict precipitation. The chosen exogenous parameters are either effective on or relevant to the precipitation patterns. Then, several lag times, hidden layer sizes, and training algorithms for different running sums are used in order to produce best forecasts. It was shown that the performance of networks increases significantly by importing more external factors as inputs. The bigger time scales also exhibited better performances. In all the five time scales, smaller lag times (especially one month), bigger hidden layer sizes (especially between 31 and 40), and GDX training algorithm presented the best performance. The highest obtained performance was presented by the network with 10 inputs, 1 month lag, 36 hidden layers, and CGF training method in 18 months running sum with R ² of 0.93.     

Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting

Artificial neural networks (ANNs) are used by hydrologists and engineers to forecast flows at the outlet of a watershed. They are employed in particular where hydrological data are limited. Despite these developments, practitioners still prefer conventional hydrological models. This study applied the standard conceptual HEC-HMS’s soil moisture accounting (SMA) algorithm and the multi layer perceptron (MLP) for forecasting daily outflows at the outlet of Khosrow Shirin watershed in Iran. The MLP [optimized with the scaled conjugate gradient] used the logistic and tangent sigmoid activation functions resulting into 12 ANNs. The R ² and RMSE values for the best trained MPLs using the tangent and logistic sigmoid transfer function were 0.87, 1.875 m³ s^?1 and 0.81, 2.297 m³ s^?1, respectively. The results showed that MLPs optimized with the tangent sigmoid predicted peak flows and annual flood volumes more accurately than the HEC-HMS model with the SMA algorithm, with R ² and RMSE values equal to 0.87, 0.84 and 1.875 and 2.1 m³ s^?1, respectively. Also, an MLP is easier to develop due to using a simple trial and error procedure. Practitioners of hydrologic modeling and flood flow forecasting may consider this study as an example of the capability of the ANN for real world flow forecasting.     

Predicting discharge coefficient of compound broad-crested weir by using genetic programming (GP) and artificial neural network (ANN) techniques

Compound broad-crested weir is a typical hydraulic structure that provides flow control and measurements at different flow depths. Compound broad-crested weir mainly consists of two sections; first, relatively small inner rectangular section for measuring low flows, and a wide rectangular section at higher flow depths. In this paper, series of laboratory experiments was performed to investigate the potential effects of length of crest in flow direction, and step height of broad-crested weir of rectangular compound cross-section on the discharge coefficient. For this purpose, 15 different physical models of broad-crested weirs with rectangular compound cross-sections were tested for a wide range of discharge values. The results of examination for computing discharge coefficient were yielded by using multiple regression equations based on the dimensional analysis. Then, the results obtained were also compared with genetic programming (GP) and artificial neural network (ANN) techniques to investigate the applicability, ability, and accuracy of these procedures. Comparison of results from the GP and ANN procedures clearly indicates that the ANN technique is less efficient in comparison with the GP algorithm, for the determination of discharge coefficient. To examine the accuracy of the results yielded from the GP and ANN procedures, two performance indicators (determination coefficient (R ²) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of GP technique sound satisfactory regarding the performance indicators (R ²?=?0.952 and RMSE?=?0.065) with less deviation from the numerical values.     

An experimental investigation of density-stratified inertial gravity currents

Turbidity currents and pyroclastic density currents may originate as stratified flows or develop stratification during propagation. Analogue, density‐stratified laboratory currents are described, using layers of salt solutions with different concentrations and depths to create the initial vertical stratification. The evolving structure of the flow depends on the distribution of the driving buoyancy between the layers, B* (proportional to the layer volumes and densities), and their density ratio, ρ*. When the lower layer contains more salt than the upper layer, and so has a greater proportion of the driving buoyancy (B* < 0·5), this layer can run ahead leading to streamwise or longitudinal stratification (ρ*→0), or the layers can mix to produce a homogeneous current (ρ*→1). If the upper layer contains more salt and thus buoyancy (B* > 0·5), this layer travels to the nose of the current by mixing into the back of the head along the body/wake density interface to produce a homogeneous flow (ρ*→1) or overtaking, leading to streamwise stratification (ρ*→0). Timescales describing the mixing between the layers and the streamwise separation of the layers are used to understand these flow behaviours and are in accordance with the experimental observations. Distance–time measurements of the flow front show that strongly stratified flows initially travel faster than weakly stratified flows but, during their later stages, they travel more slowly. In natural flows that are stratified in concentration and grain size, internal features, such as stepwise grading, gradual upward fining and reverse grading, could be produced depending on B* and ρ*. Stratification may also be expected to affect interactions with topography and overall fan architecture.     

Artificial neural network study of observed pattern of scour depth around bridge piers

An artificial neural networks (ANN) model is developed to study the observed pattern of local scour at bridge piers using an FHWA (Federal Highway Administration) data set composed of 380 measurements at 56 bridges in 13 states. Various ANN estimates of observed pier scour depth on different choices of input variables are examined. Reducing the number of variables from 14 to 9 has negligible effect on the coefficient of determination, R², (0.73 vs. 0.72). Further sensitivity analysis indicates that pier scour depth can be estimated using only four variables: pier shape and skew, flow depth and velocity with a coefficient of determination of 0.81, suggesting that inclusion of some variables actually diminishes the quality of ANN predictions of short term observed pattern of scour. The ANN estimates indicate that flow depth and flow velocity make up 66% of the coefficient of determination.     

Phosphorus transport in shallow groundwater in peri-urban Kampala, Uganda: results from field and laboratory measurements

To understand Phosphorus (P) sources and transport processes in the subsurface in Bwaise III Parish, Kampala, P attenuation and adsorption capacities of soils were studied in situ and from laboratory measurements. Relationships between sorption parameters and soil matrix properties, rates and mechanism of the adsorption process and soil P fractions were also investigated. P was generally higher in the wet than the dry season, but for both seasons, the maximum was 5 mgP/l. P transport mechanisms appeared to be a combination of adsorption, precipitation, leaching from the soil media and by colloids with the latter two playing an important role in the wet season. The sorption process comprised two phases with the first stage rate constants being about fourfold those of the second stage. The Langmuir isotherm described the sorption data well (R ² ≥ 0.95) with the second soil layer exhibiting the highest sorption maximum (C _max) (average value 0.6 ± 0.17 mgP/gDW). The best prediction of C _max had organic carbon, Ca, available P and soil pH. Residual P consisting mostly of organics was the main fraction in all the layers followed by inorganic HCl-P and NaOH-P in the top and middle layers, respectively. Loosely bound P (NH₄Cl-P) was the least fraction (<0.4% of total P) in all layers indicating the high binding capacity of P by the soils. The study results suggest that P dynamics is related to Ca, Fe and organic carbon content of the soils.     

Application of neural networks to predict net present value in mining projects

Net present value (NPV) is the most popular economic indicator in evaluation of the investment projects. For the mining projects, this criterion is calculated under uncertainty associated with the relevant parameters of say commodity price, discount rate, etc. Accurate prediction of the NPV is a quite difficult process. This paper mainly deals with the development of a new model to predict NPV using artificial neural network (ANN) in the Zarshuran gold mine, Iran. Gold price (as the main product), silver price (as the byproduct), and discount rate were considered as input parameters for the ANN model. To reach an optimum architecture, different types of networks were examined on the basis of a trial and error mechanism. A neural network with architecture 3-15-10-1 and root mean square error of 0.092 is found to be optimum. Prediction capability of the proposed model was examined through computing determination coefficient (R ²?=?0.987) between predicted and real NPVs. Absolute error of US$0.1 million and relative error of 1.4 % also confirmed powerfulness of the developed ANN model. According to sensitivity analysis, it was observed that the gold price is the most effective and discount rate is the least effective parameter on the NPV.     

Residual and Fully Softened Strength Evaluation of Soils using Artificial Neural Networks

A backpropagation artificial neural network (ANN) model is developed to predict the secant friction angle of residual and fully softened soils, using data reported by Stark et al. (J Geotech Geoenviron Eng ASCE 131:575–588, 2005). In the ANN model, index properties such as liquid limit, plastic limit, activity, clay fraction and effective normal stress are used as input variables while secant residual friction angle is used as output variable. The model is verified using data that were not used for model training and testing. The results also indicate that the secant residual friction angle of cohesive soils can be predicted quite accurately using liquid limit, clay fraction and effective normal stress as input variables with R ² = 0.93. The sensitivity analysis results indicate that plastic limit and activity have no appreciable effect on ANN predicted secant friction angles. The secant friction angle predictions of the ANN model were also compared with those of Stark’s et al. (2005) curves and the empirical formulas suggested for the same data sets by Wright (Evaluation of soil shear strengths for slope and retaining wall stability with emphasis on high plasticity clays, 2005). The comparison shows that the ANN model predictions are very close to those suggested by the Stark et al. (2005) curves but much better than the prediction of Wright’s (2005) empirical equations. The results also show that ANN is an alternative powerful tool to predict the secant friction angle of soils.     

Burden prediction in blasting operation using rock geomechanical properties

Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E _a), mean relative error (E _r), and determination coefficient (R ²) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden).     

Mathematical expression of discharge capacity of compound open channels using MARS technique

In this paper, analytical methods, artificial neural network (ANN) and multivariate adaptive regression splines (MARS) techniques were utilised to estimate the discharge capacity of compound open channels (COC). To this end, related datasets were collected from literature. The results showed that the divided channel method with a coefficient of determination (R ²) value of 0.76 and root mean square error (RMSE) value of 0.162 has the best performance, among the various analytical methods tested. The performance of applied soft computing models with R ²=0.97 and RMSE = 0.03 was found to be more accurate than analytical approaches. Comparison of MARS with the ANN model, in terms of developed discrepancy ratio (DDR) index, showed that the accuracy of MARS model was better than that of MLP model. Reviewing the structure of the derived MARS model showed that the longitudinal slope of the channel (S), relative flow depth (H _r ) and relative area (A _r ) have a high impact on modelling and forecasting the discharge capacity of COCs.     

Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA–GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years’ worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA–GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA–ANN models. The results indicate that the SARIMA–GEP model (R ²=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA–ANN (R ²=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA–GEP over the SARIMA–ANN model.     

Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model

Digital soil mapping relies on field observations, laboratory measurements and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties. The study was undertaken in a hilly watershed in the Indian Himalayan region of Mandi district, Himachal Pradesh for mapping soil nutrients by employing artificial neural network (ANN), a potent data mining technique. Soil samples collected from the surface layer (0–15 cm) of 75 locations in the watershed, through grid sampling approach during the fallow period of November 2015, were preprocessed and analysed for various soil nutrients like soil organic carbon (SOC), nitrogen (N) and phosphorus (P). Spectral indices like Colouration Index, Brightness Index, Hue Index and Redness Index derived from Landsat 8 satellite data and terrain parameters such as Terrain Wetness Index, Stream Power Index and slope using CartoDEM (30 m) were used. Spectral and terrain indices sensitive to different nutrients were identified using correlation analysis and thereafter used for predictive modelling of nutrients using ANN technique by employing feed-forward neural network with backpropagation network architecture and Levenberg–Marquardt training algorithm. The prediction of SOC was obtained with an R² of 0.83 and mean squared error (MSE) of 0.05, whereas for available nitrogen, it was achieved with an R² value of 0.62 and MSE of 0.0006. The prediction accuracy for phosphorus was low, since the phosphorus content in the area was far below the normal P values of typical Indian soils and thus the R² value observed was only 0.511. The attempts to develop prediction models for available potassium (K) and clay (%) failed to give satisfactory results. The developed models were validated using independent data sets and used for mapping the spatial distribution of SOC and N in the watershed.     

Neural network prediction of nitrate in groundwater of Harran Plain, Turkey

Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination from the uncontrolled discharge of sewage, agricultural and industrial activities. Faulty planning and mismanagement of irrigation schemes are the principle reasons of groundwater quality deterioration. This study presents an artificial neural network (ANN) model predicting concentration of nitrate, the most common pollutant in shallow aquifers, in groundwater of Harran Plain. The samples from 24 observation wells were monthly analysed for 1 year. Nitrate was found in almost all groundwater samples to be significantly above the maximum allowable concentration of 50 mg/L, probably due to the excessive use of artificial fertilizers in intensive agricultural activities. Easily measurable parameters such as temperature, electrical conductivity, groundwater level and pH were used as input parameters in the ANN-based nitrate prediction. The best back-propagation (BP) algorithm and neuron numbers were determined for optimization of the model architecture. The Levenberg–Marquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 25. The model tracked the experimental data very closely (R = 0.93). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed model application.     

Comparison of FFNN and ANFIS models for estimating groundwater level

Prediction of water level is an important task for groundwater planning and management when the water balance consistently tends toward negative values. In Maheshwaram watershed situated in the Ranga Reddy District of Andhra Pradesh, groundwater is overexploited, and groundwater resources management requires complete understanding of the dynamic nature of groundwater flow. Yet, the dynamic nature of groundwater flow is continually changing in response to human and climatic stresses, and the groundwater system is too intricate, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models are introduced into groundwater science as a powerful, flexible, statistical modeling technique to address complex pattern recognition problems. This study presents the comparison of two methods, i.e., feed-forward neural network (FFNN) trained with Levenberg–Marquardt (LM) algorithm compared with a fuzzy logic adaptive network-based fuzzy inference system (ANFIS) model for better accuracy of the estimation of the groundwater levels of the Maheshwaram watershed. The statistical indices used in the analysis were the root mean square error (RMSE), regression coefficient (R ²) and error variation (EV).The results show that FFNN-LM and ANFIS models provide better accuracy (RMSE = 4.45 and 4.94, respectively, R ² is 93% for both models) for estimating groundwater levels well in advance for the above location.     

Diagenesis of Upper Carboniferous rocks in the Ouachita foreland shelf in mid-continent USA: an overview of widespread effects of a Variscan-equivalent orogeny

Diagenesis of Upper Carboniferous foreland shelf rocks in southeastern Kansas took place at temperatures as high as 100–150° C at a depth of less than 2 km. High temperatures are the result of the long distance (hundreds of kilometers) advection of groundwater related to collisional orogeny in the Ouachita tectonic belt to the south. Orogenic activity in the Ouachita area was broadly Late Carboniferous, equivalent to the Variscan activity of Europe. Mississippi Valley-type Pb-Zn deposits and oil and gas fields in the US midcontinent and elsewhere are commonly attributed to regional groundwater flow resulting from such collisional events. This paper describes the diagenesis and thermal effects in sandstone and limestone of Upper Carboniferous siliciclastic and limestone-shale cyclothems, the purported confining layer of a supposed regional aquifer. Diagenesis took place in early, intermediate, and late stages. Many intermediate and late stage events in the sandstones have equivalents in the limestones, suggesting that the causes were regional. The sandstone paragenesis includes siderite cement (early stage), quartz overgrowths (intermediate stage), dissolution of feldspar and carbonates, followed by minor Fe calcite, pore-filling kaolinite and sub-poikilotopic Ca ankerite (late stage). The limestone paragenesis includes calcite cement (early stage); megaquartz, chalcedony, and Fe calcite spar (intermediate stage); and dissolution, Ca-Fe dolomite and kaolinite (late stage). The R _m value of vitrinite shows a regional average of 0.6–0.7%; Rock-Eval T _maX suggests a comparable degree of organic maturity. The T _h of aqueous fluid inclusions in late stage Ca-Fe-Mg carbonates ranges from 90 to 160° and T _mice indicates very saline water (>200000 ppm NaCl equivalent); ¹⁸O suggests that the water is of basinal origin. Local warm spots have higher R _m, T _max, and T _h. The results constrain numerical models of regional fluid migration, which is widely viewed as an artesian flow from recharge areas in the Ouachita belt across the foreland basin onto the foreland shelf area. Such models must account for heating effects that extend at least 500 km from the orogenic front and affect both supposed aquifer beds and the overlying supposed confining layer. Warm spots indicate either more rapid or more prolonged flow locally. T _h and T _mice data show the highest temperatures coincided with high salinity fluids.     

An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia

Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural network (ANN) techniques are being increasingly used for flood modeling. Previously, this ANN method was frequently used for hydrological and flood modeling by taking rainfall as input and runoff data as output, usually without taking into consideration of other flood causative factors. The specific objective of this study is to develop a flood model using various flood causative factors using ANN techniques and geographic information system (GIS) to modeling and simulate flood-prone areas in the southern part of Peninsular Malaysia. The ANN model for this study was developed in MATLAB using seven flood causative factors. Relevant thematic layers (including rainfall, slope, elevation, flow accumulation, soil, land use, and geology) are generated using GIS, remote sensing data, and field surveys. In the context of objective weight assignments, the ANN is used to directly produce water levels and then the flood map is constructed in GIS. To measure the performance of the model, four criteria performances, including a coefficient of determination (R ²), the sum squared error, the mean square error, and the root mean square error are used. The verification results showed satisfactory agreement between the predicted and the real hydrological records. The results of this study could be used to help local and national government plan for the future and develop appropriate (to the local environmental conditions) new infrastructure to protect the lives and property of the people of Johor.     

Meteorological noise in crustal gas emission and relevance to geochemical exploration

The emission of gas from the earth's crust is a complex process influenced by meteorological and seasonal processes which must be understood for effective application of gas emission to geochemical exploration. Free mercury vapor emission and radon emanation are being measured in a shallow instrument vault at a single nonmineralized site in order to evaluate these influences on gas emission.Mercury concentrations in the instrument vault average 9.5 ng/m³ and range from < 1 ng/m³ to 53 ng/m³ with a strong seasonal effect. Mercury has a direct relationship to vault temperature, air temperature, soil temperature, barometric pressure, water table, and the frozen or thawed state of the soil. Air and soil temperature, barometric pressure, and relative humidity are most important in influencing mercury emission while soil moisture is also important in radon emanation. Diurnal cycles are common but do not occur on all days. A heavy precipitation event on a dry soil seals the soil resulting in a rise in mercury concentration. Precipitation on a soil that is already wet does not increase mercury emission because of the compensation caused by lowering of the soil temperature by the precipitation event. Freezing of the soil changes the physical state of the vault-soil-soil gas-atmosphere system and emits the lowest concentrations of mercury. Phase lag effects are likely important. Stepwise multiple regression of mercury as dependent variable with meteorological and seasonal parameters as independent variables gives a cumulative R value of 0.563 and R² of 0.317. The short-term noise coupled with phase lags are an important factor.The radon measurements integrated over weekly intervals smooth out much of the short-term noise. Stepwise multiple regression of radon as dependent variable with meteorological and seasonal parameters as independent variables gives a cumulative R value of 0.967 and R² of 0.934. In this portion of the study the variation in the radon emanation is adequately predicted by meteorological and seasonal parameters.     

The influence of subsurface hydrology on nutrient supply and smooth cordgrass (Spartina alterniflora) production in a developing barrier island marsh

The supply of nutrients from surface and subsurface water flow into the root zone was measured in a developing barrier island marsh in Virginia. We hypothesize that high production of tall-formSpartina alterniflora in the lower intertidal zone is due to a greater nitrogen input supplied by a larger subsurface flux. Individual nitrogen inputs to the tall-form and short-formS. alterniflora root zones were calculated from water flow rates into the root zone and the nutrient concentration corresponding to the source of the flow. Total dissolved inorganic nitrogen (DIN) input (as ammonium and nitrate) was then calculated using a summation of the hourly nutrient inputs to the root zone over the entire tidal cycle based on hydrologic and nutrient data collected throughout the growing season (April–August) of 1993 and 1994. Additionally, horizontal water flow into the lower intertidal marsh was reduced experimentally to determine its effects on nutrient input and plant growth. Total ammonium (NH₄ ⁺) input to the tall-formS. alterniflora root zone (168 μmoles 6 h^?1) was significantly greater relative to the short-form (45 μmoles 6 h^?1) during flood tide. Total NH₄ ⁺ input was not significantly different between growth forms during ebb tide, and total nitrate (NO₃ ^?) and total DIN input were not significantly different between growth forms during either tidal stage. During tidal flooding, vertical flow from below the root zone accounted for 71% and horizontal flow from the adjacent mudflat accounted for 19% of the total NH₄ ⁺ input to the tall-formS. alterniflora root zone. Infiltration of flooding water accounted for 15% more of the total NO₃ ^? input relative to the total NH₄ ⁺ input at both zones on flood tide. During ebb tide, vertical flow from below the root zone still accounted for the majority of NH₄ ⁺ and NO₃ ^? input to both growth forms. After vertical flow, horizontal subsurface flow from upgradient accounted for the next largest percentages of NH₄ ⁺ and NO₃ ^? input to both growth forms during ebb tide. After 2 yr of interrupted subsurface horizontal flow to the tall-formS. alterniflora root zone, height and nitrogen content of leaf tissue of treatment plants were only slightly, but significantly, lower than control plants. The results suggest that a dynamic supply of DIN (as influenced by subsurface water flows) is a more accurate depiction of nutrient supply to macrophytes in this developing marsh, relative to standing stock nutrient concentrations. The dynamic subsurface supply of DIN may play a role in spatial patterns of abovegroundS. alterniflora production, but determination of additional nitrogen inputs and the role of belowground production on nitrogen demand need to also be considered.     

Determining Spatial and Temporal Inputs of Freshwater,Including Submarine Groundwater Discharge,to a Subtropical Estuary Using Geochemical Tracers,Biscayne Bay,South Florida

Geochemical mixing models were used to decipher the dominant source of freshwater (rainfall, canal discharge, or groundwater discharge) to Biscayne Bay, an estuary in south Florida. Discrete samples of precipitation, canal water, groundwater, and bay surface water were collected monthly for 2 years and analyzed for salinity, stable isotopes of oxygen and hydrogen, and Sr²⁺/Ca²⁺ concentrations. These geochemical tracers were used in three separate mixing models and then combined to trace the magnitude and timing of the freshwater inputs to the estuary. Fresh groundwater had an isotopic signature (δ ¹⁸O = −2.66‰, δD −7.60‰) similar to rainfall (δ ¹⁸O = −2.86‰, δD = −4.78‰). Canal water had a heavy isotopic signature (δ ¹⁸O = −0.46‰, δD = −2.48‰) due to evaporation. This made it possible to use stable isotopes of oxygen and hydrogen to separate canal water from precipitation and groundwater as a source of freshwater into the bay. A second model using Sr²⁺/Ca²⁺ ratios was developed to discern fresh groundwater inputs from precipitation inputs. Groundwater had a Sr²⁺/Ca²⁺ ratio of 0.07, while precipitation had a dissimilar ratio of 0.89. When combined, these models showed a freshwater input ratio of canal/precipitation/groundwater of 37%:53%:10% in the wet season and 40%:55%:5% in the dry season with an error of ±25%. For a bay-wide water budget that includes saltwater and freshwater mixing, fresh groundwater accounts for 1–2% of the total fresh and saline water input.     

Experimental study of sand streaks formed in turbulent boundary layers

Fine- to medium-grained sand transported as bedload moves in lanes parallel to the flow that are thought to be preserved as parting lineation. A series of six flume experiments was designed to discover the morphology and spacing of these lanes, here called sand streaks, as functions of local shear velocity, U* (9 × 10^-3 to 4.8 × 10^-2 m s^-1), depth (5 × 10^-2 and 9.5 × 10^-2 m), mean grain diameter (150, 200, 290, 1380 μm), and sediment bedload concentration (0.0–0.39). Low U* flows produce predominantly straight, non-intersecting sand streaks, moderate U* flows produce sub-parallel and en échelon sand streaks, and moderate to high U* flows produce wavy sand streaks and secondary streaks with a spacing an order of magnitude larger. The wavy sand streaks are thought to be composed of sand grains in suspension close to the bed. An upper grain-size limit for the sand streak structure occurs at a grain size between 290 and 1380μm. The spacings of the fine-and medium-grained sand streaks, at low to moderate U* (0.9 × 10^-2 to 3 × 10^-2m s^-1), are similar to those predicted for low-speed fluid streaks, although the fine-grained sand forms more closely-spaced streaks than the medium-grained sand. The spacings of sand streaks formed at moderate to high U* and at bedload concentrations greater than 0.15, are wider than those predicted for the low-speed fluid streaks. The wider spacing is thought to reflect a new type of flow immediately above the moving bed layer in which the formation of low-speed streaks is inhibited. This results from an increase in either grain concentration or grain size. The spacing of parting lineation, also wider than that predicted for low-speed streaks, may reflect this.