Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India

Two non-parametric kriging methods such as indicator kriging and probability kriging were compared and used to estimate the probability of concentrations of Cu, Fe, and Mn higher than a threshold value in groundwater. In indicator kriging, experimental semivariogram values were fitted well in spherical model for Fe and Mn. Exponential model was found to be best for all the metals in probability kriging and for Cu in indicator kriging. The probability maps of all the metals exhibited an increasing risk of pollution over the entire study area. Probability kriging estimator incorporates the information about order relations which the indicator kriging does not, has improved the accuracy of estimating the probability of metal concentrations in groundwater being higher than a threshold value. Evaluation of these two spatial interpolation methods through mean error (ME), mean square error (MSE), kriged reduced mean error (KRME), and kriged reduced mean square error (KRMSE) showed 3.52% better performance of probability kriging over indicator kriging. The combined result of these two kriging method indicated that on an average 26.34%, 65.36%, and 99.55% area for Cu, Fe, and Mn, respectively, are coming under the risk zone with probability of exceedance from a cutoff value is 0.6 or more. The groundwater quality map pictorially represents groundwater zones as ??desirable?? or ??undesirable?? for drinking. Thus the geostatistical approach is very much helpful for the planners and decision makers to devise policy guidelines for efficient management of the groundwater resources so as to enhance groundwater recharge and minimize the pollution level.     

Assessment of groundwater pollution in West Delhi, India using geostatistical approach

The exploration, exploitation, and unscientific management of groundwater resources in the National Capital Territory (NCT) of Delhi, India have posed a serious threat of reduction in quantity and deterioration of quality. The objective of the study is to determine the groundwater quality and to assess the risk of groundwater pollution at Najafgarh, NCT of Delhi. The groundwater quality parameters were analyzed from the existing wells of the Najafgarh and the thematic maps were generated using geostatistical concepts. Ordinary kriging and indicator kriging methods were used as geostatistical approach for preparation of thematic maps of the groundwater quality parameters such as bicarbonate, calcium, chloride, electrical conductivity (EC), magnesium, nitrate, sodium, and sulphate with concentrations equal or greater than their respective groundwater pollution cutoff value. Experimental semivariogram values were fitted well in spherical model for the water quality parameters, such as bicarbonate, chloride, EC, magnesium, sodium, and sulphate and in exponential model for calcium and nitrate. The thematic maps of all the groundwater quality parameters exhibited an increasing trend of pollution from the northern and western part of the study area towards the southern and eastern part. The concentration was highest at the southernmost part of the study area but it could not reflect correctly the groundwater pollution status. The indicator kriging method is useful to assess the risk of groundwater pollution by giving the conditional probability of concentrations of different chemical parameters exceeding their cutoff values. Thus, risk assessment of groundwater pollution is useful for proper management of groundwater resources and minimizing the pollution threat.     

Assessing Spatial, Temporal, and Analytical Variation of Groundwater Chemistry in a Large Nuclear Complex, USA

Statistical analyses were applied at the Hanford Site, USA, to assess groundwater contamination problems that included (1) determining local backgrounds to ascertain whether a facility is affecting the groundwater quality and (2) determining a ‘pre-Hanford' groundwater background to allow formulation of background-based cleanup standards. The primary purpose of this paper is to extend the random effects models for (1) assessing the spatial, temporal, and analytical variability of groundwater background measurements; (2) demonstrating that the usual variance estimate s ², which ignores the variance components, is a biased estimator; (3) providing formulas for calculating the amount of bias; and (4) recommending monitoring strategies to reduce the uncertainty in estimating the average background concentrations. A case study is provided. Results indicate that (1) without considering spatial and temporal variability, there is a high probability of false positives, resulting in unnecessary remediation and/or monitoring expenses; (2) the most effective way to reduce the uncertainty in estimating the average background, and enhance the power of the statistical tests in general, is to increase the number of background wells; and (3) background for a specific constituent should be considered as a statistical distribution, not as a single value or threshold. The methods and the related analysis of variance tables discussed in this paper can be used as diagnostic tools in documenting the extent of inherent spatial and/or temporal variation and to help select an appropriate statistical method for testing purposes.     

Use of multivariate indicator kriging methods for assessing groundwater contamination extents for irrigation

Multivariate geostatistical approaches have been applied extensively in characterizing risks and uncertainty of pollutant concentrations exceeding anthropogenic regulatory limits. Spatially delineating an extent of contamination potential is considerably critical for regional groundwater resources protection and utilization. This study used multivariate indicator kriging (MVIK) to determine spatial patterns of contamination extents in groundwater for irrigation and made a predicted comparison between two types of MVIK, including MVIK of multiplying indicator variables (MVIK-M) and of averaging indicator variables (MVIK-A). A cross-validation procedure was adopted to examine the performance of predicted errors, and various probability thresholds used to calculate ratios of declared pollution area to total area were explored for the two MVIK methods. The assessed results reveal that the northern and central aquifers have excellent groundwater quality for irrigation use. Results obtained through a cross-validation procedure indicate that MVIK-M is more robust than MVIK-A. Furthermore, a low ratio of declared pollution area to total area in MVIK-A may result in an unrealistic and unreliable probability used to determine extents of pollutants. Therefore, this study suggests using MVIK-M to probabilistically determine extents of pollutants in groundwater.     

Assessment of hydrogeochemical characteristics of groundwater in parts of Hindon–Yamuna interfluve region,Baghpat District,Western Uttar Pradesh

The present study has been carried out to assess groundwater quality in parts of Hindon–Yamuna interfluve region of western Uttar Pradesh. Fifty-five groundwater samples were collected from hand pumps in post-monsoon 2005 and pre-monsoon 2006 period, respectively, covering an area of about 1,345 km². Physical and chemical parameters of groundwater such as electrical conductivity, pH, total dissolved solid, Na, K, Ca, Mg, HCO₃, Cl, and SO₄ were determined. Concentration of the chemical constituents in groundwater of the study area varies spatially and temporarily. Interpretation of analytical data of major ion chemistry helps to identify three chemical types of groundwater i.e. ‘mixed’, ‘mixed bicarbonate’ and ‘alkali bicarbonate’ types. The species likely to occur in groundwater of the study area are Ca-HCO₃, Mg-HCO₃, Ca-SO₄, Na-Cl, Na-SO₄, Na-HCO₃, K-Cl, and some other possible species of K, depending on its abundance. The groundwater of the study area comes under the category of moderately hard to very hard, mildly acidic to slightly alkaline in nature. There is anomalously high concentration of major ions, particularly, Na, K, SO₄, and Cl. High SO₄ and K values may be related to anthropogenic influences, rather than through some natural process. Sodium along with Cl may be added to the system through sewage pollution and leachate percolation.     

Spatiotemporal variation of groundwater quality using integrated multivariate statistical and geostatistical approaches in Amol–Babol Plain,Iran

In recent years, groundwater quality has become a global concern due to its effect on human life and natural ecosystems. To assess the groundwater quality in the Amol–Babol Plain, a total of 308 water samples were collected during wet and dry seasons in 2009. The samples were analysed for their physico-chemical and biological constituents. Multivariate statistical analysis and geostatistical techniques were applied to assess the spatial and temporal variabilities of groundwater quality and to identify the main factors and sources of contamination. Principal component analysis (PCA) revealed that seven factors explained around 75 % of the total variance, which highlighted salinity, hardness and biological pollution as the dominant factors affecting the groundwater quality in the Plain. Two-way analysis of variance (ANOVA) was conducted on the dataset to evaluate the spatio-temporal variation. The results showed that there were no significant temporal variations between the two seasons, which explained the similarity between six component factors in dry and wet seasons based on the PCA results. There are also significant spatial differences (p?>?0.05) of the parameters under study, including salinity, potassium, sulphate and dissolved oxygen in the plain. The least significant difference (LSD) test revealed that groundwater salinity in the eastern region is significantly different to the central and western side of the study area. Finally, multivariate analysis and geostatistical techniques were combined as an effective method for demonstrating the spatial structure of multivariate spatial data. It was concluded that multiple natural processes and anthropogenic activities were the main sources of groundwater salinization, hardness and microbiological contamination of the study area.     

The geostatistic-based spatial distribution variations of soil salts under long-term wastewater irrigation

The purpose of this study was to determine and evaluate the spatial changes in soil salinity by using geostatistical methods. The study focused on the suburb area of Beijing, where urban development led to water shortage and accelerated wastewater reuse to farm irrigation for more than 30 years. The data were then processed by GIS using three different interpolation techniques of ordinary kriging (OK), disjunctive kriging (DK), and universal kriging (UK). The normality test and overall trend analysis were applied for each interpolation technique to select the best fitted model for soil parameters. Results showed that OK was suitable for soil sodium adsorption ratio (SAR) and Na⁺ interpolation; UK was suitable for soil Cl^? and pH; DK was suitable for soil Ca²⁺. The nugget-to-sill ratio was applied to evaluate the effects of structural and stochastic factors. The maps showed that the areas of non-saline soil and slight salinity soil accounted for 6.39 and 93.61 %, respectively. The spatial distribution and accumulation of soil salt were significantly affected by the irrigation probabilities and drainage situation under long-term wastewater irrigation.     

Regional assessment of groundwater quality for drinking purpose

Owing to limited surface water during a long-term drought, this work attempted to locate clean and safe groundwater in the Choushui River alluvial fan of Taiwan based on drinking-water quality standards. Because aquifers contained several pollutants, multivariate indicator kriging (MVIK) was adopted to integrate the multiple pollutants in groundwater based on drinking- and raw-water quality standards and to explore spatial uncertainty. According to probabilities estimated by MVIK, safe zones were determined under four treatment conditions—no treatment; ammonium–N and iron removal; manganese and arsenic removal; and ammonium–N, iron, manganese, and arsenic removal. The analyzed results reveal that groundwater in the study area is not appropriate for drinking use without any treatments because of high ammonium–N, iron, manganese, and/or arsenic concentrations. After ammonium–N, iron, manganese, and arsenic removed, about 81.9–94.9% of total areas can extract safe groundwater for drinking. The proximal-fan, central mid-fan, southern mid-fan, and northern regions are the excellent locations to pump safe groundwater for drinking after treatment. Deep aquifers of exceeding 200 m depth have wider regions to obtain excellent groundwater than shallow aquifers do.     

Application and evaluation of kriging and cokriging methods on groundwater depth mapping

Groundwater and water resources management play a key role in conserving the sustainable conditions in arid and semi-arid regions. Applying some techniques that can reveal the critical and hot conditions of water resources seem necessary. In this study, kriging and cokriging methods were evaluated for mapping the groundwater depth across a plain in which has experienced different climatic conditions (dry, wet, and normal) and consequently high variations in groundwater depth in a 12 year led in maximum, minimum, and mean depths. During this period groundwater depth has considerable fluctuations. Results obtained from geostatistical analysis showed that groundwater depth varies spatially in different climatic conditions. Furthermore, the calculated RMSE showed that cokriging approach was more accurate than kriging in mapping the groundwater depth though there was not a distinct difference. As a whole, kriging underestimated the real groundwater depth for dry, wet, and normal conditions by 5.5, 2.2, and 5.3%, while cokriging underestimations were 3.3, 2, and 2.2%, respectively; which showed the unbiasedness in estimations. Results implied that in the study area farming and cultivation in dry conditions needs more attention due to higher variability in groundwater depth in short distances compared to the other climate conditions. It is believed that geostatistical approaches are reliable tools for water resources managers and water authorities to allocate groundwater resources in different environmental conditions.     

A probabilistic methodology to assess the risk of groundwater quality degradation

An approach to assess the risk of groundwater quality degradation with regard to fixed standards, based on DisjunctiveKriging (DK) is presented. The DK allows one to evaluate the Conditional Probability (CP) of overriding a given threshold of concentration of a pollutant at a given time, and at a generic point in a consideredgroundwater system. The result of such investigation over the considered area can be plotted in form of maps of spatial risk. By repeating this analysis at different times, severalspatial riskmaps will be produced, one for each consideredtime. By means of non-parametric statistics, the temporal trendof the CPs can be evaluated at every point of the considered area. The trend index, assessed by means of a sort of classification of the trend values obtained as described above,can be superimposed on the most recent values of the spatialrisk (i.e.: the most recent values of probability). Consequentlya classification of the risk of groundwater quality degradationresults with which to weigh both the spatial distribution and thetemporal behaviour of the probability to exceed a given standardthreshold. The methodology has been applied to values of nitrateconcentration sampled in the monitoring well network of theModena plain, northern Italy. This area is characterised by intensive agricultural exploitation and hog breeding along withindustrial and civil developments. The influence of agriculture on groundwater results in a high nitrate pollution that limitsits use for potable purposes.     

Geostatistical Uncertainty Modelling for the Environmental Hazard Assessment During Single Erosive Rainstorm Events

This paper presents an environmental hazard assessment to account the impacts of single rainstorm variability on river-torrential landscape identified as potentially vulnerable mainly to erosional soil degradation processes. An algorithm for the characterisation of this impact, called Erosive Hazard Index (EHI), is developed with a less expensive methodology. In EHI modelling, we assume that the river-torrential system has adapted to the natural hydrological regime, and a sudden fluctuation in this regime, especially those exceeding thresholds for an acceptable range of flexibility, may have disastrous consequences for the mountain environment. The hazard analysis links key rainstorm energy variables expressed as a single-storm erosion index (EI_sto), with impact thresholds identified using an intensity pattern model. Afterwards, the conditional probabilities of exceeding these thresholds are spatially assessed using non-parametric geostatistical techinques, known as indicator kriging. The approach was applied to a test site in river-torrential landscape of the Southern Italy (Benevento province) for 13 November 1997 rainstorm event.     

Groundwater quality assessment and its correlation with gastroenteritis using GIS: a case study of Rawal Town,Rawalpindi, Pakistan

Majority of the people of Pakistan get drinking water from groundwater source. Nearly 40 % of the total ailments reported in Pakistan are the result of dirty drinking water. Every summer, thousands of patients suffer from acute gastroenteritis in the Rawal Town. Therefore, a study was designed to generate a water quality index map of the Rawal Town and identify the relationship between bacteriological water quality and socio-economic indicators with gastroenteritis in the study area. Water quality and gastroenteritis patient data were collected by surveying the 262 tubewells and the major hospitals in the Rawal Town. The collected spatial data was analyzed by using ArcGIS spatial analyst (Moran’s I spatial autocorrelation) and geostatistical analysis tools (inverse distance weighted, radial basis function, kriging, and cokriging). The water quality index (WQI) for the study area was computed using pH, turbidity, total dissolved solids, calcium, hardness, alkalinity, and chloride values of the 262 tubewells. The results of Moran’s I spatial autocorrelation showed that the groundwater physicochemical parameters were clustered. Among IDW, radial basis function, and kriging and cokriging interpolation techniques, cokriging showed the lowest root mean square error. Cokriging was used to make the spatial distribution maps of water quality parameters. The WQI results showed that more than half of the tubewells in the Rawal Town were providing “poor” to “unfit” drinking water. The Pearson’s coefficient of correlation for gastroenteritis with fecal coliform was found significant (P?P?P?相似文献   

Heavy metals assessment and identification of their sources in agricultural soils of Southern Tehran, Iran

A detailed investigation was conducted to evaluate heavy metal sources and their spatial distribution in agricultural fields in the south of Tehran using statistics, geostatistics, and a geographic information system. The content of Cd, Cu, Co, Pb, Zn, Cr, and Ni were determined in 106 samples. The results showed that the primary inputs of Cr, Co, and Ni were due to pedogenic factors, while the inputs of Zn, Pb, and Cu were due to anthropogenic sources. Cd was associated with distinct sources, such as agricultural and industrial pollution. Ordinary kriging was carried out to map the spatial patters of heavy metals, and disjunctive kriging was used to quantify the probability of heavy metal concentrations higher than their recommended threshold values. The results show that Cd, Cu, Ni, and Zn exhibit pollution risk in the study area. The sources of the high pollution levels evaluated were related to the use of urban and industrial wastewater and agricultural practices. These results are useful for the development of proper management strategies for remediation practices in the polluted area.     

Patterns of Spatial Autocorrelation in Stream Water Chemistry

Geostatistical models are typically based on symmetric straight-line distance, which fails to represent the spatial configuration, connectivity, directionality, and relative position of sites in a stream network. Freshwater ecologists have explored spatial patterns in stream networks using hydrologic distance measures and new geostatistical methodologies have recently been developed that enable directional hydrologic distance measures to be considered. The purpose of this study was to quantify patterns of spatial correlation in stream water chemistry using three distance measures: straight-line distance, symmetric hydrologic distance, and weighted asymmetric hydrologic distance. We used a dataset collected in Maryland, USA to develop both general linear models and geostatistical models (based on the three distance measures) for acid neutralizing capacity, conductivity, pH, nitrate, sulfate, temperature, dissolved oxygen, and dissolved organic carbon. The spatial AICC methodology allowed us to fit the autocorrelation and covariate parameters simultaneously and to select the model with the most support in the data. We used the universal kriging algorithm to generate geostatistical model predictions. We found that spatial correlation exists in stream chemistry data at a relatively coarse scale and that geostatistical models consistently improved the accuracy of model predictions. More than one distance measure performed well for most chemical response variables, but straight-line distance appears to be the most suitable distance measure for regional geostatistical modeling. It may be necessary to develop new survey designs that more fully capture spatial correlation at a variety of scales to improve the use of weighted asymmetric hydrologic distance measures in regional geostatistical models.     

Drought Hazard and Desertification Management in the Drylands of Southern Africa

Droughts have been occurring persistently in southern African dryland regions for over a century. The impacts of droughts on people, their domesticated animals, wildlife, rangelands and cropped lands have been shown to be astronomical. If left alone the rangelands often recover after the calamity, however human occupation has led to irreversible damage. Even though some communities have evolved viable and sustainable coping mechanisms, recent times have seen weakened coping strategies leading to loss of life in most of the 10 countries in southern Africa. While land degradation has many inter-related causes and effects, drought-related effects have proven most difficult to manage and/or overcome. Drought-related land degradation or desertification poses a huge threat to sustainable land and resource management in the region. The paper examines appropriate drought mitigating initiatives, linking them to land tenure and land management practices. Numerous interventions targeted at reducing poverty and improvement in resource management have failed to achieve desired effects due to rigidity and imposition, and failure of the external interveners to recognise and incorporate indigenous peoples’ preferences and coping strategies. Non-governmental organisations and authorities’ willingness to institute drought and desertification combating measures are reviewed, highlighting the role that community action plays in reducing adverse effects in the region. Linkages to trade patterns that perpetuate poverty and unwise use of resources are discussed. Adopting ‘people centred’ mitigating measures is emphasised. Success rests with both the people in the ‘south’ and those in the ‘north’. What is required is an informed global action.     

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.     

Spatial Distribution of Forest Fires and Controlling Factors in Andhra Pradesh, India Using Spot Satellite Datasets

Fires are one of the major causes of forest disturbance and destruction in several dry deciduous forests of southern India. In this study, we use remote sensing data sets in conjunction with topographic, vegetation, climate and socioeconomic factors for determining the potential causes of forest fires in Andhra Pradesh, India. Spatial patterns in fire characteristics were analyzed using SPOT satellite remote sensing datasets. We then used nineteen different metrics in concurrence with fire count datasets in a robust statistical framework to arrive at a predictive model that best explained the variation in fire counts across diverse geographical and climatic gradients. Results suggested that, of all the states in India, fires in Andhra Pradesh constituted nearly 13.53% of total fires. District wise estimates of fire counts for Andhra Pradesh suggested that, Adilabad, Cuddapah, Kurnool, Prakasham and Mehbubnagar had relatively highest number of fires compared to others. Results from statistical analysis suggested that of the nineteen parameters, population density, demand of metabolic energy (DME), compound topographic index, slope, aspect, average temperature of the warmest quarter (ATWQ) along with literacy rate explained 61.1% of total variation in fire datasets. Among these, DME and literacy rate were found to be negative predictors of forest fires. In overall, this study represents the first statewide effort that evaluated the causative factors of fire at district level using biophysical and socioeconomic datasets. Results from this study identify important biophysical and socioeconomic factors for assessing ‘forest fire danger’ in the study area. Our results also identify potential ‘hotspots’ of fire risk, where fire protection measures can be taken in advance. Further this study also demonstrate the usefulness of best-subset regression approach integrated with GIS, as an effective method to assess ‘where and when’ forest fires will most likely occur.     

Spatial patterns of heavy metals in soil under different geological structures and land uses for assessing metal enrichments

One hundred and thirty composite soil samples were collected from Hamedan county, Iran to characterize the spatial distribution and trace the sources of heavy metals including As, Cd, Co, Cr, Cu, Ni, Pb, V, Zn, and Fe. The multivariate gap statistical analysis was used; for interrelation of spatial patterns of pollution, the disjunctive kriging and geoenrichment factor (EF_G) techniques were applied. Heavy metals and soil properties were grouped using agglomerative hierarchical clustering and gap statistic. Principal component analysis was used for identification of the source of metals in a set of data. Geostatistics was used for the geospatial data processing. Based on the comparison between the original data and background values of the ten metals, the disjunctive kriging and EF_G techniques were used to quantify their geospatial patterns and assess the contamination levels of the heavy metals. The spatial distribution map combined with the statistical analysis showed that the main source of Cr, Co, Ni, Zn, Pb, and V in group A land use (agriculture, rocky, and urban) was geogenic; the origin of As, Cd, and Cu was industrial and agricultural activities (anthropogenic sources). In group B land use (rangeland and orchards), the origin of metals (Cr, Co, Ni, Zn, and V) was mainly controlled by natural factors and As, Cd, Cu, and Pb had been added by organic factors. In group C land use (water), the origin of most heavy metals is natural without anthropogenic sources. The Cd and As pollution was relatively more serious in different land use. The EF_G technique used confirmed the anthropogenic influence of heavy metal pollution. All metals showed concentrations substantially higher than their background values, suggesting anthropogenic pollution.     

Simulation and Trend Analysis of the Water Quality Monitoring Daily Data in Nestos River Delta. Contribution to the Sustainable Management and Results for the Years 2000–2002

The transboundary River Nestos in the Balkan Peninsula is a surface water resource shared by Hellas and Bulgaria. The Public Power Corporation of Hellas (DEH) proceeded to the dams' construction of Thesaurus in 1997 and Platanovrissi in 2000, to satisfy the increased needs for power production and irrigation in the Regions of Eastern Macedonia and Thrace in the Hellenic Territory. DEH following the Ministerial Agreement of the Hellenic Parliament ‘`KYA 18492/19—09—1996’' funded a series of Research Projects concerned on the monitoring of the water quantity and quality data of Nestos from the Hellenic-Bulgarian borders to its estuaries in the Thracian sea. ‘`PERSEAS’' Research Group from Aristotle University of Thessaloniki, carried out the research, design, construction, installation, operation and maintenance of the ‘`R.E.MO.S.’' (Remote Environmental MOnitoring System) networks. Three REMOS networks have been installed in the areas of (a) the River Nestos deltaic channel, (b) Thesaurus dam-lake in the intramountainous valley and (c) Potamoi (Despat) and Pagoneri (Nestos) villages close to the borders between Hellas and Bulgaria. They record water level (H), water and air temperature (T), water conductivity (ECw), Redox potential (RP) and dissolved oxygen (DO) on a 24h basis, since the beginning of the year 2000. The research carried out in this paper, is focused on the REMOS station in the final course of Nestos in the deltaic area. The continuous monitoring and the data analysis yield useful results for the quality and quantity of the hydrologic regime of Nestos after the dams' construction, as well as for the trends detected of the quality parameters (ECw, RP and DO) and the water level, using the nonparametric Spearman's criterion. The best fitted model of time trend, for each variable, was chosen. The statistical sample of each one of the quality variables consisted of about 1000 values based on daily measures on a three years monitoring program (1/1/2000—31/12/2002). Further research and analysis for the other network stations of REMOS should provide useful results for the sustainable management of the transboundary River Nestos.