Decision support model for assessing aquifer pollution hazard and prioritizing groundwater resources management in the wet Pampa plain, Argentina

This paper gives an account of the implementation of a decision support system for assessing aquifer pollution hazard and prioritizing subwatersheds for groundwater resources management in the southeastern Pampa plain of Argentina. The use of this system is demonstrated with an example from Dulce Stream Basin (1,000 km² encompassing 27 subwatersheds), which has high level of agricultural activities and extensive available data regarding aquifer geology. In the logic model, aquifer pollution hazard is assessed as a function of two primary topics: groundwater and soil conditions. This logic model shows the state of each evaluated landscape with respect to aquifer pollution hazard based mainly on the parameters of the DRASTIC and GOD models. The decision model allows prioritizing subwatersheds for groundwater resources management according to three main criteria including farming activities, agrochemical application, and irrigation use. Stakeholder participation, through interviews, in combination with expert judgment was used to select and weight each criterion. The resulting subwatershed priority map, by combining the logic and decision models, allowed identifying five subwatersheds in the upper and middle basin as the main aquifer protection areas. The results reasonably fit the natural conditions of the basin, identifying those subwatersheds with shallow water depth, loam–loam silt texture soil media and pasture land cover in the middle basin, and others with intensive agricultural activity, coinciding with the natural recharge area to the aquifer system. Major difficulties and some recommendations of applying this methodology in real-world situations are discussed.     

A method to the impact assessment of the returning grazing land to grassland project on regional eco-environmental vulnerability

The Chinese government has conducted the Returning Grazing Land to Grassland Project (RGLGP) across large portions of grasslands from western China since 2003. In order to explore and understand the impact in the grassland's eco-environment during the RGLGP, we utilized Projection Pursuit Model (PPM) and Geographic Information System (GIS) to develop a spatial assessment model to examine the ecological vulnerability of the grassland. Our results include five indications: (1) it is practical to apply the spatial PPM on ecological vulnerability assessment for the grassland. This methodology avoids creating an artificial hypothesis, thereby providing objective results that successfully execute a multi-index assessment process and analysis under non-linear systems in eco-environments; (2) the spatial PPM is not only capable of evaluating regional eco-environmental vulnerability in a quantitative way, but also can quantitatively demonstrate the degree of effect in each evaluation index for regional eco-environmental vulnerability; (3) the eco-environment of the Xianshui River Basin falls into the medium range level. The normalized difference vegetation index (NDVI) and land use cover and change (LUCC) crucially influence the Xianshui River Basin's eco-environmental vulnerability. Generally, in the Xianshui River Basin, regional eco-environmental conditions improved during 2000 and 2010. The RGLGP positively affected NDVI and LUCC structure, thereby promoting the enhancement of the regional eco-environment; (4) the Xianshui River Basin divides its ecological vulnerability across different levels; therefore our study investigates three ecological regions and proposes specific suggestions for each in order to assist in eco-environmental protection and rehabilitation; and lastly that (5) the spatial PPM established by this study has the potential to be applied on all types of grassland eco-environmental vulnerability assessments under the RGLGP and under the similar conditions in the Returning Agriculture Land to Forest Project (RALFP). However, when establishing an eco-environmental vulnerability assessment model, it is necessary to choose suitable evaluation indexes in accordance with regional eco-environmental characteristics.     

Identification of effective best management practices in sediment yield diminution using GeoWEPP: the Kasilian watershed case study

Identifying areas that are susceptible to soil erosion is crucial for water resource planning and management efforts. Furthermore, modeling has proven helpful in recognizing and monitoring high-risk areas at the watershed scale. The Water Erosion Prediction Project (WEPP) geospatial interface (GeoWEPP) software integrates GIS with the WEPP to analyze the spatial variation in soil loss, and it has been used as a modeling tool to determine the areas that are most prone to soil erosion and to evaluate best management practices for the Kasilian watershed in Iran. As much as 62.4 % of the agronomic land in the Kasilian watershed is affected by a high magnitude of erosion (>5 t/ha). On the basis of this study, by using soybeans, high fertilization levels, and the drill-no-tillage system, reductions of erosion by almost 32.68–34.02 % are perceivable in three critical subwatersheds that are located in the cultivated lands. Also, it is projected that reductions in the production of sediment in the range of about 36.7–47.1 % are achievable by structural management within two critical, upland subwatersheds. So, by utilizing the best management strategies, sediment yield can be lowered and the conservation of soil and water is feasible at the watershed scale. These results objectively indicate that GeoWEPP can be efficaciously used for evaluating effective management practices for developing watershed conservation.     

Groundwater vulnerability assessment using DRASTIC and Pesticide DRASTIC models in intense agriculture area of the Gangetic plains,India

Delineating areas susceptible to contamination from anthropogenic sources form an important component of sustainable management of groundwater resources. The present research aims at estimating vulnerability of groundwater by application of DRASTIC and Pesticide DRASTIC models in the southern part of the Gangetic plains in the state of Bihar. The DRASTIC and Pesticide DRASTIC models have considered seven parameters viz. depth to water level, net recharge, aquifer material, soil material, topography, impact of vadose zone and hydraulic conductivity. A third model, Pesticide DRASTIC LU has been adopted by adding land use as an additional parameter, to assess its impact on vulnerability zonation. The DRASTIC model indicated two vulnerable categories, moderate and high, while the Pesticide DRASTIC model revealed moderate, high and very high vulnerable categories. Out of the parameters used, depth to water level affected the vulnerability most. The parameter caused least impact was topography in DRASTIC, while in case of Pesticide DRASTIC and Pesticide DRASTIC LU models, the parameter was hydraulic conductivity. A linear regression between groundwater NO₃ concentrations and the vulnerability zonation revealed better correlation for Pesticide DRASTIC model, emphasising the effectiveness of the model in assessing groundwater vulnerability in the study region. Considering all three models, the most vulnerable areas were found to be concentrated mainly in two zones, (i) in the south-western part along Ekangarsarai-Islampur patch and (ii) around Biharsharif-Nagarnausa area in the central part. Both zones were characterised by intensive vegetable cultivation with urban areas in between.     

A multi criteria analog model for assessing the vulnerability of rural catchments to road spills of hazardous substances

Road spills of hazardous substances are common in developing countries due to increasing industrialization and traffic accidents, and represent a serious threat to soils and water in catchments. There is abundant literature on equations describing the wash-off of pollutants from roads during a storm event and there are a number of watershed models incorporating those equations in storm water quality algorithms that route runoff and pollution yields through a drainage system towards the catchment outlet. However, methods describing catchment vulnerability to contamination by road spills based solely on biophysical parameters are scarce. These methods could be particularly attractive to managers because they can operate with a limited amount of easily collectable data, while still being able to provide important insights on the areas more prone to contamination within the studied watershed. The purpose of this paper was then to contribute with a new vulnerability model. To accomplish the goal, a selection of medium properties appearing in wash-off equations and routing algorithms were assembled and processed in a parametric framework based on multi criteria analysis to define the watershed vulnerability. However, parameters had to be adapted because wash-off equations and water quality models have been developed to operate primarily in the urban environment while the vulnerability model is meant to run in rural watersheds. The selected parameters were hillside slope, ground roughness (depending on land use), soil permeability (depending on soil type), distance to water courses and stream density. The vulnerability model is a spatially distributed algorithm that was prepared to run under the IDRISI Selva software, a GIS platform capable of handling spatial and alphanumeric data and execute the necessary terrain model, hydrographic and thematic analyses. For illustrative purposes, the vulnerability model was applied to the legally protected Environmental Protection Area (APA), located in the Uberaba region, state of Minas Gerais, Brazil. In this region, the risk of accidents causing chemical spills is preoccupying because large quantities of dangerous materials are transported in two important distribution highways while the APA is fundamental for the protection of water resources, the riverine ecosystems and remnants of native vegetation. In some tested scenarios, model results show 60% of vulnerable areas within the studied area. The most sensitive parameter to vulnerability is soil type. To prevent soils from contamination, specific measures were proposed involving minimization of land use conflicts that would presumably raise the soil's organic matter and in the sequel restore the soil's structural functions. Additionally, the present study proposed the preservation and reinforcement of riparian forests as one measure to protect the quality of surface water.     

Compositing climate change vulnerability of a Mediterranean region using spatiotemporally dynamic proxies for ecological and socioeconomic impacts and stabilities

The study presents a new methodology to quantify spatiotemporal dynamics of climate change vulnerability at a regional scale adopting a new conceptual model of vulnerability as a function of climate change impacts, ecological stability, and socioeconomic stability. Spatiotemporal trends of equally weighted proxy variables for the three vulnerability components were generated to develop a composite climate change vulnerability index (CCVI) for a Mediterranean region of Turkey combining Landsat time series data, digital elevation model (DEM)-derived data, ordinary kriging, and geographical information system. Climate change impact was based on spatiotemporal trends of August land surface temperature (LST) between 1987 and 2016. Ecological stability was based on DEM, slope, aspect, and spatiotemporal trends of normalized difference vegetation index (NDVI), while socioeconomic stability was quantified as a function of spatiotemporal trends of land cover, population density, per capita gross domestic product, and illiteracy. The zones ranked on the five classes of no-to-extreme vulnerability were identified where highly and moderately vulnerable lands covered 0.02% (12 km²) and 11.8% (6374 km²) of the study region, respectively, mostly occurring in the interior central part. The adoption of this composite CCVI approach is expected to lead to spatiotemporally dynamic policy recommendations towards sustainability and tailor preventive and mitigative measures to locally specific characteristics of coupled ecological–socioeconomic systems.     

Assessment of groundwater vulnerability in the coastal region of Oman using DRASTIC index method in GIS environment

A study was carried out to develop a vulnerability map for Barka region in the North Batina of Oman using DRASTIC vulnerability index method in GIS environment. DRASTIC layers were created using data from published reports and the seven DRASTIC layers were processed by the ArcGIS geographic information system. Finally, DRASTIC maps were created for 1995 and 2004 to understand the long-term changes in the vulnerability index. DRASTIC vulnerability maps were evaluated using groundwater quality data such as chemical and biological parameters. DRASTIC vulnerability maps of 1995 and 2004 indicate that the northern part of Barka is more vulnerable to pollution than southern part and the central part of Barka also shows high relative vulnerability which is mostly related to the high conductivity values. Moreover, the changes in water level due to high abstraction rate of groundwater reflect in the vulnerability maps and low vulnerability area is increased in the southern part during 2004 compared to 1995. Moreover, regional distribution maps of nitrate, chloride and total and fecal coliforms are well correlated with DRASTIC vulnerability maps. In contrast to this, even though DRASTIC method predicted the central part of the study region is highly vulnerable, both chemical and biological parameters show lower concentrations in this region compared to coastal belt, which is mainly due to agricultural and urban development. In Barka, urban development and agricultural activities are very high in coastal region compared to southern and central part of the study area. Hence, this study concluded that DRASTIC method is also applicable in coastal region having ubiquitous contamination sources.     

Application of artificial neural network approach and remotely sensed imagery for regional eco-environmental quality evaluation

Eco-environment quality evaluation is an important research theme in environment management. In the present study, Fuzhou city in China was selected as a study area and a limited number of 222 sampling field sites were first investigated in situ with the help of a GPS device. Every sampling site was assessed by ecological experts and given an Eco-environment Background Value (EBV) based on a scoring and ranking system. The higher the EBV, the better the ecological environmental quality. Then, three types of eco-environmental attributes that are physically-based and easily-quantifiable at a grid level were extracted: (1) remote sensing derived attributes (vegetation index, wetness index, soil brightness index, surface land temperature index), (2) meteorological attributes (annual temperature and annual precipitation), and (3) terrain attribute (elevation). A Back Propagation (BP) Artificial Neural Network (ANN) model was proposed for the EBV validation and prediction. A three-layer BP ANN model was designed to automatically learn the internal relationship using a training set of known EBV and eco-environmental attributes, followed by the application of the model for predicting EBV values across the whole study area. It was found that the performance of the BP ANN model was satisfactory and capable of an overall prediction accuracy of 82.4%, with a Kappa coefficient of 0.801 in the validation. The evaluation results showed that the eco-environmental quality of Fuzhou city is considered as satisfactory. Through analyzing the spatial correlation between the eco-environmental quality and land uses, it was found that the best eco-environmental areas were related to forest lands, whereas the urban area had the relatively worst eco-environmental quality. Human activities are still considered as a major impact on the eco-environmental quality in this area.     

Using environmental stressor information to predict the ecological status of Maryland non-tidal streams as measured by biological indicators

In Maryland, U.S., an interim framework has recentlybeen developed for using biologically based thresholds, or `biocriteria', to assess the health of nontidal streams statewide at watershed scales. The evaluation of impairment is based on indices of biological integrity from the Maryland Biological Stream Survey (MBSS). We applied logistic regression to quantify how the biotic integrity of streams at a local scale is affected by cumulative effects resulting from catchment land uses, point sources, and nearby transmission line rights-of-way. Indicators for land use were developed from the remote sensing National Land Cover Data and applied at different scales. We determined that the risk of local impairment in nontidal streams rapidly increases with increased urban land use in the catchment area. The average likelihood of failing biocriteria doubled with every 10% points increment in urban land, thus an increase in urban land use from 0 to 20% quadruples the risk of impairment. For the basins evaluated in this study, catchments with more than 40–50% urban land use had greater than 80% probability of failing biocriteria, on average. Inclusion of rights-of-way and point sources in the model did not significantly improve the fit for this data set, most likely because of their low numbers. The overall results indicate that our predictive modeling approach can help pinpoint stream ecosystems experiencing or vulnerable to degradation.     

Environmental vulnerability assessment using Grey Analytic Hierarchy Process based model

Environmental management of an area describes a policy for its systematic and sustainable environmental protection. In the present study, regional environmental vulnerability assessment in Hirakud command area of Odisha, India is envisaged based on Grey Analytic Hierarchy Process method (Grey–AHP) using integrated remote sensing (RS) and geographic information system (GIS) techniques. Grey–AHP combines the advantages of classical analytic hierarchy process (AHP) and grey clustering method for accurate estimation of weight coefficients. It is a new method for environmental vulnerability assessment. Environmental vulnerability index (EVI) uses natural, environmental and human impact related factors, e.g., soil, geology, elevation, slope, rainfall, temperature, wind speed, normalized difference vegetation index, drainage density, crop intensity, agricultural DRASTIC value, population density and road density. EVI map has been classified into four environmental vulnerability zones (EVZs) namely: ‘low’, ‘moderate’ ‘high’, and ‘extreme’ encompassing 17.87%, 44.44%, 27.81% and 9.88% of the study area, respectively. EVI map indicates that the northern part of the study area is more vulnerable from an environmental point of view. EVI map shows close correlation with elevation. Effectiveness of the zone classification is evaluated by using grey clustering method. General effectiveness is in between “better” and “common classes”. This analysis demonstrates the potential applicability of the methodology.     

水生态环境质量评价体系研究

流域水生态环境质量评价体系是为流域环境管理提供基础数据,协助管理者开展流域保护措施的重要技术支持。该文深入剖析了发达国家典型水生态环境质量评价体系,归纳分析了其体系框架、构成特点、方法适用性和借鉴意义。结合前期研究基础,对评价体系的技术内容进行了完善和补充,明确了参照点位的选择原则,补充了评价方法选择的技术路线,提出了增设和实施不同监测计划的方案,最终提出生态环境质量评价技术体系框架。     

基于广西河池市胁迫风险的土壤环境脆弱性评价

采用层次分析法划分了土壤环境各因素的敏感性和重要程度,从而建立起能科学表达土壤环境承载能力的土壤环境脆弱性评价体系。基于广西河池市指标数据,借助地理信息系统软件Arc GIS9.3、ENVI5.1、MATLAB7.0等计算平台对空间数据进行模型训练与验证,得出可视化表征土壤环境脆弱性空间分布状况的评价图件。结果表明,河池市北部、南部存在高度脆弱区,占全市国土面积的36.3%,中度脆弱区占38.3%,低度脆弱区占25.4%。总体来看,河池市大部分区域的土壤环境属中高度脆弱。     

CBERS-2B数据提取平原区细小地物系数的方法探讨

利用Landsat TM等中等分辨率影像对区域生态环境进行监测与评价时,由于影像分辨率的局限性,仍有许多地物无法解译出来,如农田中的道路、水渠等,文章主要研究了利用图像融合增强和人机交互式解译等技术方法,对CBERS-2B的CCD影像和HR影像进行处理,提取平原区农田中道路面积比系数,从而将农田中未解译的地物扣除。结果表明,CCD影像与HR影像经过融合增强处理后,可以进行平原地区村级及以上道路的提取。     

水生态环境物联网智慧监测技术发展及应用

水生态环境物联网监测是一种利用信息技术实现水生态环境数据获取与应用的智能服务.通过水生态环境监测物联网,借助传感技术、传输网络和智能联动等手段,能够及时、全面地获取流域水生态环境质量动态数据信息,提升水生态环境保护管理决策的信息化水平.在综述了水生态环境物联网智慧监测发展概况的基础上,基于物联网总体架构,重点从感知技术...     

The importance of small urbanized watersheds to pollutant loading in a large oligotrophic subalpine lake of the western USA

Urban land use has been implicated as a major contributor of nonpoint source pollution in aquatic systems. Through increased nonpoint delivery of pollutants, including constituents found in stormwater, Lake Tahoe is undergoing a marked decline in its transparency, primarily due to increasing production of algae from enhanced nutrient loading and delivery of fine particles to the lake from the watershed. In response to these findings, a regional restoration effort is underway to improve basin watersheds and the water quality in Lake Tahoe. In this study, stormwater autosamplers were used to collect flow-weighted composite samples that characterized event mean concentrations for event and nonevent conditions within a small, urbanized watershed in the Tahoe basin. An event-specified constant-concentration water quality model was then applied to the event mean concentration and continuous streamflow data to estimate pollutant loads for nitrate, nitrite, ammonia, orthophosphate, and suspended sediment. These data were compared with previously reported load estimates from 10 primary monitored streams in larger watersheds of the Tahoe basin. Results from a linear regression analysis demonstrate strong and significant relationships between watershed impervious area and pollutant loadings from Lake Tahoe watersheds. These small, urbanized watersheds and intervening zones, which only comprise 10 % of the total Lake Tahoe drainage area, include a significant portion of the total Lake Tahoe impervious area. The findings of this study suggest that small, urbanized watersheds and intervening zones are disproportionately important contributors of nonpoint source pollution, including nutrients and suspended particles.     

A vulnerability analysis in the Fei-tsui reservoir watershed in Taiwan

The vulnerability analysis method has been widely used in many environmental fields. In recent years, the tool has succeeded in comprehensive assessment of environmental problems. This study applied the vulnerability and resilience analysis method on watershed conservation and grasped the environmental change capacity that watershed could bear. The Fei-tsui reservoir watershed in Northern Taiwan provided the setting for the case study reported herein. This study considered both internal and external effect factors, including watershed vulnerability, rainfall energy and the distances between the outlet and subbasins, and developed a new index, WP, for the priority restraints strategies on the land-use activities. The land-use restraint index can be a significant criterion for watershed protection and management strategies.     

Linking Hydrogeological and Ecological Tools for an Integrated River Catchment Assessment

Ecological (biological and hydrochemical assessment) and hydrogeological (vulnerability and pollution risk mapping) tools have been combined to assess the ecological quality and hydrogeological vulnerability of an agricultural river basin. In addition, the applicability of the recently developed vulnerability assessment approach (COP method) in the particular environmental conditions was tested by comparing its results with hydroecological assessment tools (i.e., pollution metrics). Five sampling sites were selected and sampled for benthic macroinvertebrates and physicochemical variables during summer and spring. Overall, sites ranged from moderate to poor ecological quality. The results illustrated that 26% of the study area was of moderate pollution risk, while 65% was classified as of low and very low risk zones. However, the higher elevation zones where calcareous rock formations are encountered presented moderate to high pollution risk that was accredited by the ecological quality assessment. Pollution metrics facilitated from hydrochemical analysis indicated a significant association with groundwater vulnerability, thus validating vulnerability and risk estimations. This study indicated that the particular groundwater pollution risk mapping methodology and the water quality assessment indices can be well combined to provide an integrated evaluation tool at a catchment scale.     

漯河市城市生态环境质量定量评价

从自然生态环境质量、社会生态环境质量和经济生态环境质量3个方面构建了漯河市城市生态环境质量评价指标体系,采用层次分析法确定了各评价指标的权重值,借助综合评价模型计算了评价指标体系的综合指标值.并对城市生态环境质量现状进行了评价.结果显示,漯河市综合生态环境质量中等,其中,自然生态环境质量较好,社会生态环境质量中等,经济生态环境质量差.评价结果与定性评价结果相符,表明评价指标体系具有较强的科学性和实用性.依据评价结果,提出改善城市生态环境的对策和建议.     

生态环境监测与生态经济学评价

探讨了荒漠生态环境监测与生态经济学之间的关系,提出荒漠生态环境监测应与生态经济学原则相结合,注意监测人类社会经济活动对生态环境的影响,并根据生态经济学中环境因素的稀缺性、综合性和反馈性等原理对人类开发利用自然资源进行正确评价。     

Application of integrated GIS and multimedia modeling on NPS pollution evaluation

In Taiwan, nonpoint source (NPS) pollution is one of the major causes of the impairment of surface waters. I-Liao Creek, located in southern Taiwan, flows approximately 90 km and drains toward the Kaoping River. Field investigation results indicate that NPS pollution from agricultural activities is one of the main water pollution sources in the I-Liao Creek Basin. Assessing the potential of NPS pollution to assist in the planning of best management practice (BMP) is significant for improving pollution prevention and control in the I-Liao Creek Basin. In this study, land use identification in the I-Liao Creek Basin was performed by properly integrating the skills of geographic information system (GIS) and global positioning system (GPS). In this analysis, 35 types of land use patterns in the watershed area of the basin are classified with the aid of Erdas Imagine process system and ArcView GIS system. Results indicate that betel palm farms, orchard farms, and tea gardens dominate the farmland areas in the basin, and are scattered around on both sides of the river corridor. An integrated watershed management model (IWMM) was applied for simulating the water quality and evaluating NPS pollutant loads to the I-Liao Creek. The model was calibrated and verified with collected water quality and soil data, and was used to investigate potential NPS pollution management plans. Simulated results indicate that NPS pollution has significant contributions to the nutrient loads to the I-Liao Creek during the wet season. Results also reveal that NPS pollution plays an important role in the deterioration of downstream water quality and caused significant increase in nutrient loads into the basin's water bodies. Simulated results show that source control, land use management, and grassy buffer strip are applicable and feasible BMPs for NPS nutrient loads reduction. GIS system is an important method for land use identification and waste load estimation in the basin. Linking the information of land utilization with the NPS pollution simulation model may further provide essential information of potential NPS pollution for all subregions in the river basin. Results and experience obtained from this study will be helpful in designing the watershed management and NPS pollution control strategies for other similar river basins.