Terrain Discontinuity Effects in the Regional Flow of a Complex Karstified Aquifer

Karst aquifers are characterized by spatial heterogeneity due to the presence of highly permeable channels and conduits in low-permeable fractured rocks (matrix block). Recent studies have reported a close relationship between surface and subsurface water in karstic regions due to the water flow through a complicated network of paths formed by fracture intersections. Subsurface flow in karstified aquifers ranges between conduit flow, in large passages with relatively high flow velocities, and diffuse flow, in the matrix block where Darcy’s law is still valid. In this paper, we present the simulation of a complex karstified aquifer system in Crete, Greece, where the presence of main faults drastically affects the regional flow. A discrete fracture approach in conjunction with an equivalent porous medium approach was adopted to simulate the mixed flow in the area of interest. The simulation results have shown that the length and the orientation of the dominant faults, primarily during the rainy season, affect the flow field.     

How Do River Nitrate Concentrations Respond to Changes in Land-use? A Modelling Case Study of Headwaters in the River Derwent Catchment,North Yorkshire,UK

A combined semi-distributed hydrological model (CASCADE/QUESTOR) is used to evaluate the steady-state that may be achieved after changes in land-use or management and to explore what additional factors need to be considered in representing catchment processes. Two rural headwater catchments of the River Derwent (North Yorkshire, UK) were studied where significant change in land-use occurred in the 1990s and the early 2000s. Much larger increases in mean nitrate concentration (55%) were observed in the catchment with significant groundwater influence (Pickering Beck) compared with the surface water-dominated catchment (13% increase). The increases in Pickering Beck were considerably greater than could be explained by the model in terms of land-use change. Consequently, the study serves to focus attention on the long-term increases in nitrate concentration reported in major UK aquifers and the ongoing and chronic impact this trend is likely to be having on surface water concentrations. For river environments, where groundwater is a source, such trends will mask the impact of measures proposed to reduce the risk of nitrate leaching from agricultural land. Model estimates of within-channel losses account for 15–40% of nitrate entering rivers.     

Selecting Variables for Habitat Suitability of <Emphasis Type="Italic">Asellus</Emphasis> (Crustacea,Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models

This study aimed to compare different methods to analyse the contribution of individual river characteristics to predict the abundance of Asellus (Crustacea, Isopoda). Six methods which provide the relative contribution and/or the contribution profile of the input variables of artificial neural network models were therefore compared: (1) the ‘partial derivatives’ method; (2) the ‘weights’ method; (3) the ‘perturb’ method; (4) the ‘profile’ method; (5) the ‘classical stepwise’ method; (6) the ‘improved stepwise’ method. Consequently, the key variables which affect the habitat preferences of Asellus could be identified. To evaluate the performance of the artificial neural network model, the model predictions were compared with the results of a multiple linear regression analysis. The dataset consisted of 179 samples, collected over a 3-year period in the Zwalm catchment in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Asellus were used in this study. The different contribution methods seemed to give similar results concerning the order of importance of the input variables. Nevertheless, their diverse computation led to differences in sensitivity and stability of the methods and the derived outcomes on the habitat preferences. From an ecological point of view, the environmental variables describing the stream type (width, depth, stream order and distance to mouth) were the most significant variables for Asellus in the Zwalm catchment during the period 2000–2002 for all applied methods. Indirectly, one can conclude that the water quality is not the limiting factor for the survival of Asellus in the Zwalm catchment.     

A Spatially Explicit Model for Estimating Annual Average Loads of Nonpoint Source Nutrient at the Watershed Scale

The overloaded nonpoint source (NPS) nutrients in upper streams always result in the nutrient enrichment at lakes and estuaries downstream. As NPS pollution has become a serious environmental concern in watershed management, the information about nutrient output distribution across a watershed has been critical in the designing of regional development policies. But existing watershed evaluation models often encounter difficulties in application because of their complicated structures and strict requirements for the input data. In this paper, a spatially explicit and process-based model, Integrated Grid’s Exporting and Delivery model, was introduced to estimate annual in-stream nutrient levels. Each grid cell in this model was regarded as having potentials of both exporting new nutrients and trapping nutrients passing by. The combined nutrient dynamics of a grid is mainly determined by the grid’s features in land use/land cover, soil drainage, and geomorphology. This simple-concept model was tested at some basins in north Georgia in the USA. Stations in one basin were used to calibrate the model. Then an external validation was employed by applying the calibrated model to stations in the other neighbor basins. Model evaluation statistics implied the model’s validity and good performance in estimating the annual NPS nutrients’ fluxes at the watershed scale. This study also provides a promising prospect that in-stream annual nutrient loads can be accurately estimated from a few public available datasets.     

Inexact Fuzzy-Stochastic Programming for Water Resources Management Under Multiple Uncertainties

In this study, an interval-parameter fuzzy-stochastic two-stage programming (IFSTP) approach is developed for irrigation planning within an agriculture system under multiple uncertainties. A concept of the distribution with fuzzy-interval probability (DFIP) is defined to address multiple uncertainties expressed as integration of intervals, fuzzy sets, and probability distributions. IFSTP integrates the interval programming, two-stage stochastic programming, and fuzzy-stochastic programming within a general optimization framework. IFSTP incorporates the pre-regulated water resources management policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised amounts are not delivered. IFSTP is applied to an irrigation planning in a water resources management system. Solutions from IFSTP provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable solutions are generated for objective function values and decision variables; thus, a number of decision alternatives can be generated under different levels of stream flows.     

Development of Dissolved Oxygen Model for a Highly Variable Flow River: A Case Study of Ravi River in Pakistan

A framework for dissolved oxygen (DO) modeling of the Ravi River has been developed based on a combination of laboratory measurements and field and monitoring data. Both the classical Streeter-Phelps (CSP) and the modified Streeter-Phelps (MSP) models are used for DO simulations. The MSP model considers the carbonaceous biochemical oxygen demand (CBOD) and nitrogenous biochemical oxygen demand (NBOD) separately, whereas the CSP model is evaluated considering only the CBOD and NBOD is incorporated in the overall BOD utilization rate. CBOD, NBOD and BOD rates have been determined through long-term BOD analysis of five main wastewater outfalls and two surface drains discharging into the Ravi River over a 98 km stretch. Analysis by Thomas Method manifests strong coefficient of determination “R²” between 0.72 and 0.98 for all the three types of BOD rates. Sensitivity analyses have also been carried out to find out a suitable reaeration rate formula for highly variable flows in the Ravi River. The CSP model results based on classical approach of considering only CBOD show significant difference between the model predictions and field measurements suggesting that NBOD needs to be incorporated for the model development. The dissolved oxygen values calculated using the MSP model and the CSP model based on overall BOD rate are in close agreement with field measurements and are thus suitable to model DO levels in the Ravi River.     

Seagrass resource assessment using remote sensing methods in St. Joseph Sound and Clearwater Harbor,Florida, USA

In the event of a natural or anthropogenic disturbance, environmental resource managers require a reliable tool to quickly assess the spatial extent of potential damage to the seagrass resource. The temporal availability of the Landsat 5 Thematic Mapper (TM) imagery provided a suitable option to detect and assess damage of the submerged aquatic vegetation (SAV). This study examined Landsat TM imagery classification techniques to create two-class (SAV presence/absence) and three-class (SAV estimated coverage) SAV maps of the seagrass resource. The Mahalanobis Distance method achieved the highest overall accuracy (86%) and validation accuracy (68%) for delineating the seagrass resource (two-class SAV map). The Maximum Likelihood method achieved the highest overall accuracy (74%) and validation accuracy (70%) for delineating the seagrass resource three-class SAV map. The Landsat 5 TM imagery classification provided a seagrass resource map product with similar accuracy to the aerial photointerpretation maps (validation accuracy 71%). The results support the application of remote sensing methods to analyze the spatial extent of the seagrass resource.     

Comparison of some quality properties of soils around land-mined areas and adjacent agricultural fields

When agricultural lands are no longer used for agriculture and allowed to recover its natural vegetation, soil organic carbon can accumulate in the soil. Measurements of soil organic carbon and aggregate stability changes under various forms of land use are needed for the development of sustainable systems. Therefore, comparison of soil samples taken from both agricultural and nearby area close to land-mined fields where no agricultural practices have been done since 1956 can be a good approach to evaluate the effects of tillage and agriculture on soil quality. The objective of this study was to compare tillage, cropping and no tillage effects on some soil-quality parameters. Four different locations along the Turkey–Syria border were selected to determine effects of tillage and cropping on soil quality. Each location was evaluated separately because of different soil type and treatments. Comparisons were made between non-tilled and non-cropped fallow since 1956 and adjacent restricted lands that were tilled about every 2 years but not planted (T) or adjacent lands tilled and planted with wheat and lentil (P). Three samples were taken from the depths of 0–20 and 20–40 cm each site. Soil organic carbon (SOC), pH ,electrical conductivity, water soluble Ca⁺⁺, Mg⁺⁺, CO₃^-2{\rm CO}_{3}^{-2} and HCO₃^-{\rm HCO}_{3}^{-}, extractable potassium (K⁺) and sodium (Na⁺), soil texture, ammonium (NH₄⁺{\rm NH}_{4}^{+}–N) and nitrate (NO₃–N), extractable phosphorous and soil aggregate stability were determined. While the SOC contents of continuous tillage without cropping and continuous tillage and cropping were 2.2 and 11.6 g kg⁻¹, respectively, it was 30 g kg⁻¹ in non-tilled and non-planted site. Tillage of soil without the input of any plant material resulted in loss of carbon from the soil in all sites. Soil extractable NO₃−N contents of non-tilled and non-cropped sites were greatest among all treatments. Agricultural practices increased phosphorus and potassium contents in the soil profile. P₂O₅ contents of planted soils were approximately 20 to 39 times greater than those of non-tilled and non-cropped soils at different sites. FTIR spectra showed that never tilled sites had greater phenol, carboxylic acid, amide, aromatic compounds, polysaccharide and carbohydrates than other treatments.     

The effects of socioeconomic parameters on household solid-waste generation and composition in developing countries (a case study: Ahvaz,Iran)

Environment problems associated with the generation of waste are part of societal changes where households play an important role. These societal changes influence the size, structure and characteristic of given households. For the effective planning of solid-waste handling infrastructure, it is essential to know the quantity of waste generation and its composition. This paper presents the findings of a study carried out in an urban municipal area in Iran to determine the household solid-waste generation rate and waste composition based on field surveys and to determine the related socioeconomic parameters. The dependent variables were waste generation and composition, and independent variables were family size, family employment, age, number of room and education. Over 400 sample households were selected for the study using a stratified random sampling methodology and from five different socioeconomic groups. Waste collected from all groups of households were segregated and weighted. Waste generation rate was 5.4 kg/household/day. Household solid waste comprised of ten categories of wastes and with the largest component (76.9%). The generation and composition of household solid waste were correlated with family size, education level and households income. This paper adequately suggests new insights concerning the role of socioeconomic parameters in affecting the generation of household waste.     

The backend design of an environmental monitoring system upon real-time prediction of groundwater level fluctuation under the hillslope

The groundwater level represents a critical factor to evaluate hillside landslides. A monitoring system upon the real-time prediction platform with online analytical functions is important to forecast the groundwater level due to instantaneously monitored data when the heavy precipitation raises the groundwater level under the hillslope and causes instability. This study is to design the backend of an environmental monitoring system with efficient algorithms for machine learning and knowledge bank for the groundwater level fluctuation prediction. A Web-based platform upon the model-view controller-based architecture is established with technology of Web services and engineering data warehouse to support online analytical process and feedback risk assessment parameters for real-time prediction. The proposed system incorporates models of hydrological computation, machine learning, Web services, and online prediction to satisfy varieties of risk assessment requirements and approaches of hazard prevention. The rainfall data monitored from the potential landslide area at Lu-Shan, Nantou and Li-Shan, Taichung, in Taiwan, are applied to examine the system design.