Spatial variability of rainfall trends in Iran

The main objective of this paper is to analyze the spatial variability of rainfall trends using the spatial variability methods of rainfall trend patterns in Iran. The study represents a method on the effectiveness of spatial variability for predicting rainfall trend patterns variations. In rainfall trend analysis and spatial variability methods, seven techniques were used: Mann–Kendall test, Sen’s slope method, geostatistical tools as a global polynomial interpolation and the spatial autocorrelation (Global Moran’s I), high/low clustering (Getis-Ord General G), precipitation concentration index, generate spatial weights matrix tool, and activation functions of semiliner, sigmoid, bipolar sigmoid, and hyperbolic tangent in the artificial neural network technique .For the spatial variability of monthly rainfall trends, trend tests were used in 140 stations of spatial variability of rainfall trends in the 1975–2014 period. We analyzed the long and short scale spatial variability of rainfall series in Iran. Spatial variability distribution of rainfall series was depicted using geostatistical methods (ordinary kriging). Relative nugget effect (RNE) predicted from variograms which showed weak, moderate, and strong spatial variability for seasonal and annual rainfall series. Moreover, the rainfall trends at each station were examined using the trend tests at a significance level of 0.05. The results show that temporal and spatial trend patterns are different in Iran and the monthly rainfall had a downward (decreasing) trend in most stations, and the trend was statistically significant for most of the series (73.5% of the stations demonstrated a decreasing trend with 0.5 significance level). Rainfall downward trends are generally temporal-spatial patterns in Iran. The monthly variations of rainfall decreased significantly throughout eastern and central Iran, but they increased in the west and north of Iran during the studied interval. The variability patterns of monthly rainfall were statistically significant and spatially random. Activation functions in the artificial neural network models, in annual time scale, had spatially dispersed distribution with other clustering patterns. The results of this study confirm that variability of rainfall revealing diverse patterns over Iran should be controlled mainly by trend patterns in the west and north parts and by random and dispersed patterns in the central, southern, and eastern parts.     

Identifying the spatial and temporal distribution characteristics of precipitation in Iran

Precipitation has been regarded as one of the most important meteorological parameters affecting human activities. The findings of research studies confirm that the variability and fluctuation in precipitation has direct impacts on climate of a given region. The purpose of the present study was to investigate long-term patterns in precipitation variation in Iran. To this end, the available data related to rainfall in Iran over the past half century (1957–2007) were retrieved from APHRODITE database in order to analyze the spatial-temporal variations of precipitation. The statistical test performed on the collected data included spatial auto-correlation, global Moran’s index, local Moran’s index, and hotspots. The results obtained showed that the precipitation variation in Iran followed a high clustering pattern. More specifically, the results of the local Moran’s index and hotspot analysis performed on the data revealed that the precipitation along the Caspian Sea coast and western and southwestern parts of the country had a positive spatial auto-correlation while the precipitation variation in some parts of the central Iran and along the coastline of the country showed a negative spatial auto-correlation. Moreover, the findings of the present study showed that the climate change continued during the decades under study, with clustering patterns of precipitation moving from the southern parts of the country upward towards the coastal regions of the Caspian Sea and the regions in the outskirt of Zagros Mountains.     

Precipitation spatial variability and dry areas temporal stability for different elevation classes in the Macta basin (Algeria)

The water availability relies primarily on precipitation whose spatial and temporal variability depends on meteorological and topographic attributes. Water becomes a precious natural resource, especially in semiarid areas. Generally, decisions on water resources are made on the whole watershed, but the variability of precipitation is related to topography. The work was aimed at quantifying the spatial variability of annual precipitation for a 40-year-long time series in the Macta basin (Algeria) by using a geostatistical approach and to detect the temporal stability of dry areas. To assess if annual precipitation variability could depend upon the elevation, the study area was divided into five geographical units (polygons) based on elevation and polygon kriging was applied. For each hydrologic year, the standardized relative difference of precipitation (SRDP) was evaluated and dry areas identified. The temporal stability of SRDP with elevation was assessed using the Spearman rank coefficient. Geostatistical approach showed different variability structures of annual precipitation over the considered period. Results highlighted differences in SRDP within the geographical units located at different elevation and the non-stability of dry periods with time within the same polygon. A remarkable dry tendency was assessed in the northern polygon, while the polygons at higher elevations were dominated by temporal instability. The spatio-temporal non-stability of dry areas might be attributed to the change in general atmospheric circulation in North Africa over the last decades and to the nonlinear interaction among precipitation and orography. The identification of dry areas can help decision-makers to plan management and conservation programs in Algeria.     

Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study: Fasa Plain,Iran)

Groundwater is considered as one of the most important sources for water supply in Iran. The Fasa Plain in Fars Province, Southern Iran is one of the major areas of wheat production using groundwater for irrigation. A large population also uses local groundwater for drinking purposes. Therefore, in this study, this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis (CA), Discriminant Analysis (DA), and Principal Component Analysis (PCA). Water quality data was monitored at 22 different wells, for five years (2009-2014) with 10 water quality parameters. By using cluster analysis, the sampling wells were grouped into two clusters with distinct water qualities at different locations. The Lasso Discriminant Analysis (LDA) technique was used to assess the spatial variability of water quality. Based on the results, all of the variables except sodium absorption ratio (SAR) are effective in the LDA model with all variables affording 92.80% correct assignation to discriminate between the clusters from the primary 10 variables. Principal component (PC) analysis and factor analysis reduced the complex data matrix into two main components, accounting for more than 95.93% of the total variance. The first PC contained the parameters of TH, Ca²⁺, and Mg²⁺. Therefore, the first dominant factor was hardness. In the second PC, Cl^-, SAR, and Na⁺ were the dominant parameters, which may indicate salinity. The originally acquired factors illustrate natural (existence of geological formations) and anthropogenic (improper disposal of domestic and agricultural wastes) factors which affect the groundwater quality.     

1965-2015年新疆夏季不同等级降水的空间分布特征

根据新疆51个台站1965-2015年夏季逐日降水资料,将降水划分为小雨、中雨及大雨3个等级,分析了新疆近51 a夏季不同等级降水量、降水日数及降水强度的空间分布特征,并讨论了各等级降水日、降水量及降水强度与总降水量的空间相似程度以及各等级降水对夏季总降水的贡献。结果表明：新疆降水主要集中在夏季,并以小雨为主。以天山山脉为界,南北两疆降水空间分布存在明显差异,北疆夏季降水量（日）占年降水量（日）的36%~45%（36%~39%）,南疆夏季降水量（日）占年降水量（日）的51%~63%（48%~60%）;新疆夏季不同等级降水量、降水日及降水强度的空间分布不均匀。新疆夏季总降水量与各等级降水量的空间相似系数最为密切,与各等级降水强度的空间相似系数相对较小;新疆夏季小雨贡献率最大,中雨其次,大雨最小,夏季降水量和降水日的变化主要受小雨的影响。     

Analysis of pervasive precipitation in similar gradient areas of Iran

Precipitation is a major climatic element with high spatial variations. Temporal and spatial variations may differ in large and small scales. It is, therefore, of utmost importance to study areas with similar gradients in terms of precipitation patterns in order to shed light on the complexities of precipitation variations. In the present study, attempts were made to identify areas with similar gradients experiencing the same precipitation pattern over a 50-year period (1964–2013). To this end, data were collected from synoptic stations in Iran in two phases (i.e., 1434 stations in the first phase and 673 stations in the second one). Alexanderson’s technique was adopted to examine sudden changes in precipitation patterns. The results showed that five regions with similar gradients could be identified in terms of precipitation patterns: negative and high variations, negative and moderate variations, positive and high variations, positive and moderate variations, and little or no variations. The distribution of such regions indicated that the regions with positive trends experienced more annual variations and had further spatial distribution. Furthermore, the findings revealed that the regions with negative precipitation patterns experienced more sudden changes in comparison with those with positive precipitation patterns. Additionally, more variations were observed in the precipitation patterns in recent years.     

Spatial and temporal variability of rainfall: case of Bizerte-Ichkeul Basin (Northern Tunisia)

This study examines spatial and temporal variability of rainfall in Bizerte-Ichkeul Watershed. The basin, located in the extreme north of Tunisia, covers an area of 3084 km². Thirteen rainfall stations, with continuous monthly precipitation records over the period (1970–2011), were considered in the analysis. Two methods were used. In the first, the dimensionless standardized precipitation ratio is applied to examine precipitation temporal variation. The second method is represented by continuous wavelet analysis for the precipitation spatial analysis and the identification of the origin of its variability. The study of temporal variability of annual rainfall showed severe persistent and recurrent drought episodes over the period (1977–2001). Wavelet analysis resulted in detecting the modes and origins of precipitation variability. Three energy bands were clearly identified: (1, 2–4, and 4–8 years) for the entire watershed. The visualization of the power distribution showed that the observed modes of variability are different in their power distributions from one station to another. The approach adopted allowed the identification of two groups with the same precipitation frequency and temporal variation. These groups were defined according to the difference in occurrence of the frequency band for each station.     

Relation between GRACE-derived surface mass variations and precipitation over Australia

Surface mass changes (SMCs) obtained from time-variable gravity observations of the Gravity Recovery and Climate Experiment (GRACE) satellite mission and precipitation data from the Australian Bureau of Metrology and the Tropical Rainfall Measurement Mission are analysed over the Australian continent to determine whether there is a statistically significant correlation between them. The multiple linear regression analysis and the principal-component analysis techniques are applied in order to reveal the spatial and temporal variability of each data set separately as well as their mutual relationships. The study provides results and their statistical significance for the whole of Australia including the Murray Darling Basin in the southeast. The results suggest a significant decrease in water storage in the southeast of Australia and a dominant annual cycle over the majority of the continent for the four year period considered (January 2003 to December 2006), both in the surface mass and rainfall time series. The study revealed a direct relation between the data sets over most parts of Australia as confirmed by visual comparison and correlation analysis. When compared with precipitation data GRACE-derived SMCs exhibit smoother spatial and temporal variations. The latter is better suited to detect long-term trends in the presence of strong annual signals, which can adversely affect long-term trend estimates. Results regarding the magnitude of the annual signal suggest that only about a fourth of the precipitation's water masses remain sufficiently long in an area to be detected as a gravity change. The respective phases of the annual signals show an average time lag of about 40 days between precipitation and SMCs, suggesting that it takes about one to two months until a temporal gravity observation can detect a precipitation event.     

Revisited rainfall network design: evaluation of heuristic versus entropy theory methods

Rain gauges are installed to measure pointwise precipitation and provide a comprehensive perspective of its spatiotemporal variations. Selection of an efficient and reliable rainfall monitoring network is a key role to reduce its maintenance and handling cost. The main purpose of the current paper is to compare efficiencies of various network design methods. The applied methods are entropy theory (as probabilistic multi-criteria decision-making) and genetic algorithm (as one of the heuristic methods) with three objective functions. Also, two classical (ordinary kriging; OK) and modern (Bayesian maximum entropy; BME) spatial simulation methods were undertaken to provide a comprehensive spatial simulation of precipitation. The proposed assessment was applied on spatial mean annual precipitation variability in the Namak Lake watershed located in the central part of Iran. The final efficiency of developed network design methods is evaluated in terms of three criteria known as mass estimation error, total error, and spatial bias of estimated rainfall. Based on the results, different network distributions have been proposed by the methods. Despite the reliability of the heuristic approach in nonlinear optimization due to its mathematical principle, the results indicated that the network design based on entropy theory can be used to estimate long-term mean annual precipitation more reliably and accurately. Results of the mass estimation error have shown 78 and 83% superiority of the entropy theory approach from the worst approach obtained from the OK and BME methods, respectively.     

Use of artificial neural network for spatial rainfall analysis

In the present study, the precipitation data measured at 23 rain gauge stations over the Achaia County, Greece, were used to estimate the spatial distribution of the mean annual precipitation values over a specific catchment area. The objective of this work was achieved by programming an Artificial Neural Network (ANN) that uses the feed-forward back-propagation algorithm as an alternative interpolating technique. A Geographic Information System (GIS) was utilized to process the data derived by the ANN and to create a continuous surface that represented the spatial mean annual precipitation distribution. The ANN introduced an optimization procedure that was implemented during training, adjusting the hidden number of neurons and the convergence of the ANN in order to select the best network architecture. The performance of the ANN was evaluated using three standard statistical evaluation criteria applied to the study area and showed good performance. The outcomes were also compared with the results obtained from a previous study in the area of research which used a linear regression analysis for the estimation of the mean annual precipitation values giving more accurate results. The information and knowledge gained from the present study could improve the accuracy of analysis concerning hydrology and hydrogeological models, ground water studies, flood related applications and climate analysis studies.     

Spatio-temporal variations in precipitation on the Huang-Huai-Hai Plain from 1963 to 2012

Spatio-temporal variations in precipitation are affecting agricultural production in China in the context of climate change. Based on daily precipitation data from 63 national meteorological stations on the Huang-Huai-Hai Plain from 1963 to 2012, this paper analysed the spatio-temporal variations in precipitation in terms of precipitation days and intensity, using spatial interpolation, linear trend estimation and wavelet analysis. The results indicated that: (i) from 1963 to 2012, the number of annual precipitation days and intensity decreased gradually from the southeast to the northwest. Additionally, the distribution of the extreme precipitation index was similar to that of the annual precipitation index; (ii) the number of annual precipitation days and heavy precipitation days gradually decreased, while precipitation intensity and extreme precipitation days and extreme rainfall intensity remained relatively stable or decreased. The spatial patterns of annual variation trends were considerably different. The annual precipitation days and intensity trends are consistent with the overall trend, while that of the extreme rainfall index in some regions differs from the overall trend; (iii) the precipitation index displayed different periodic oscillations during the period, and the precipitation index values differed at different time scales. However, all the precipitation index values exhibited a 28-yr oscillation.     

叶尔羌河源流区近50年来年径流变化及其对气候变化的响应

  基于新疆卡群水文站和塔什库尔干气象站1959～2005年的观测资料,运用小波分析和统计分析相结合的方法,从多时间尺度研究叶尔羌河源流区近50年来年径流的非线性变化趋势,以及径流对气候变化的响应。结果表明: 1）年径流、气温和降水的主要变化周期几乎一致,年径流和年降水量都存在24年的主要变化周期,而年平均气温则是23年。2）年径流量表现出具有时间尺度依赖性的非线性变化趋势,与区域气候变化密切相关。3）年径流变化是区域气候变化响应的结果,从8（2³）年、4（2²）年和2（2¹）年时间尺度上来看,年径流量与年平均气温和年降水量之间存在显著的线性相关关系。     

三江源区大气水汽含量时空特征及其转化变化

利用1979-2016年ERA-Interim再分析资料提供的1°×1°水汽通量和大气可降水量（PWV）数据,采用相关性分析、趋势分析法、累积距平及反距离加权（IDW）等方法,研究三江源地区PWV与水汽通量的时空分布特征和降水转化率（PCE）。结果表明:①过去38年来,经、纬向多年平均水汽通量分别为2.0 kg/（m·s）、10.3 kg/（m·s）,水汽通量纬向增幅高于经向,水汽在纬向汇入为主,经向输出为主;② PWV呈微弱增多趋势,年平均PWV为1 791.6~2 278.9 mm,季节平均PWV为122.2~1 134.2 mm,不同季节内空间差异明显;③三江源区多年平均PCE为24.6%,1989年最高,达32.8%;季节与多年平均PCE空间分布一致,都表现出由东南向西北递减的变化特征,季节分布变化差异大;④该地区空中水资源丰富但自然PCE低,开发潜力大,应用前景广阔。     

Mapping heatwave vulnerability in Korea

Analysis of event-based soil erosion magnitude with special return periods is essential to appropriately design strategies and adopt soil conservation practices. However, the spatiotemporal variations of soil erosion with different return periods, especially at national level, have not been adequately considered. Therefore, the present study aimed to zone rainfall erosivity index (R factor) as the most dynamic factor affecting variability of soil erosion rate, with different return periods in monthly, seasonal and annual time scales in Iran. Toward this attempt, the kinetic energy and maximum 30-min intensity (I ₃₀) over 12,000 available and accessible events of 70 stations were calculated during the common period of 1984–2004 and the corresponding R factor of the Universal Soil Loss Equation was then computed. Subsequently, the best-fitted frequency distributions were determined in all stations in three time scales using the EasyFit Software. The R factor was accordingly estimated for 2-, 5-, 10-, 25- and 50-year return periods. In addition, the inverse distance weighting technique was employed to determine and analyze the spatial variability patterns of R factor in different time scales using geographic information system. The results indicated that the frequency distributions fitted to study data were different in study time scales due to variability of spatiotemporal patterns of R factor. In addition, no specific spatial pattern of R factor could be recognized for different return periods and time scales. The average annual R factor was also found 1.41 MJ mm ha^?1 h^?1, whereas the respective R factor for different respective return periods of 2, 5, 10, 25 and 50 years was obtained 1.47, 2.62, 3.35, 4.48 and 5.54 MJ mm ha^?1 h^?1. These findings can be used for suitable decision making and effective environmental planning for land management Iran countrywide.     

Comparison of mineralization pattern of geochemical data in spatial and position-scale domain using new DWT- PCA approach

In the current research to determine the mineralization pattern and discuss the mineralization components, the information of position - scale domain of geochemical data has been analyzed. A new method is proposed based on coupling discrete wavelet transforms (DWT) and principal component analysis (PCA) for mineralization elements forecasting applications. The results of this study indicate the potential of DWT–PCA method for geochemical data processing. Wavelet transform (WT), as a multi-spectral analysis method, can decompose the spatial and temporal signals into different frequencies. The features of mineralization can be identified using the position - scale domain of geochemical data that may not be achievable in spatial domain. The geochemical data from the Dalli region have been processed in the spatial domain using PCA. The surface geochemical data of 30 elements have been transformed to position–scale domain using two-dimensional discrete wavelet transform (2DDWT). Wavelet functions (WFs) of Haar, Coiflet2, Biorthogonal3.3 and Symlet7 have been applied separately to decompose the geochemical data to high and low frequencies in one level. To obtain more accurate and complete information of mineralization, a new index has been presented based on wavelet coefficients. Based on this new index, significant results have been obtained by using PCA of the index. The coefficients distribution map (CDM) as a new exploratory criterion has been generated based on 2DDWT to show the geochemical distribution map (GDM). Finally, the results of WT have been compared with the results of spatial domain and the best method of wavelet for interpretation of geochemical data has been introduced. The results of geochemical data analysis by DWT–PCA approach have been confirmed by the exploratory drillings in the study area.     

Spatial and temporal variability of Holocene temperature in the North Atlantic region

The early Holocene climate of the North Atlantic region was influenced by two boundary conditions that were fundamentally different from the present: the presence of the decaying Laurentide Ice Sheet (LIS) and higher than present summer solar insolation. In order to assess spatial and temporal patterns of Holocene climate evolution across this region, we collated quantitative paleotemperature records at sub-millennial resolution and synthesized their temporal variability using principal components analysis (PCA). The analysis reveals considerable spatial variability, most notably in the time-transgressive expression of the Holocene thermal maximum (HTM). Most of the region, but especially areas peripheral to the Labrador Sea and hence closest to the locus of LIS disintegration, experienced maximum Holocene temperatures that lagged peak summer insolation by 1000-3000 years. Many sites from the northeastern North Atlantic sector, including the Nordic Seas and Scandinavia, either warmed in phase with maximum summer insolation (11,000-9000 years ago) or were less strongly lagged than the Baffin Bay-Labrador Sea region. These spatially complex patterns of Holocene climate development, which are defined by the PCA, resulted from the interplay between final decay of the LIS and solar insolation forcing.     

Application of multivariate spatial analysis in scale-based distribution and source study of PAHs in the topsoil: an example from Tianjin,China

Polycyclic aromatic hydrocarbons (PAHs) in soil originate from various sources under different spatial scales. Coregionalization analysis is more revealing than univariate geostatistical analysis. Scale-dependent spatial features of variables reflect different sources of spatial variability. In this study, 188 topsoil samples in the Tianjin area were collected. The contents of 16 PAHs and soil background properties were determined for all samples. A multivariate geostatistical approach was used for multi-scale spatial analysis for PAH compounds. Results show that coal combustion was the major source for the spatial distribution patterns of PAHs in the topsoil of the studied area. It worked mainly at the short-range scale (5–10 km). Significant spatial variation patterns were identified. In contrast, no significant spatial distribution trends at the nugget (0–5 km) or long-range scales (10–50 km) were seen. Long-range transport and site contamination of PAHs might not be key contributors in forming the distribution pattern of PAHs in the topsoil of Tianjin area.     

Application of classical statistics,logratio transformation and multifractal approaches to delineate geochemical anomalies in the Zarshuran gold district,NW Iran

The aim of this study is to discriminate the geochemical anomalies in the Zarshuran district, NW Iran, using different geochemical methods and present a more useful method where anomalous areas better coincide with the geological features. For this methods of delineation, geochemical anomalies were compared using geological features, occupied area of anomalies respect to the total study area, and field observations. Frequency based analysis such as mean + 2SDEV and median + 2MAD and concentration–area (C–A) multifractal methods were adopted for estimating thresholds and separating geochemical anomalies in uni-element data, as well as multi-element ones. Threshold values obtained from mean + 2SDEV and median + 2MAD, from original point geochemical data, are smaller than those of the pixel values; this may be due to the stronger variance of pixel values. In addition, the C–A multifractal method, as a useful tool to identify weak geochemical anomalies, was applied for defining the threshold values. Robust principal component analysis (RPCA) methods coupled with isometric log-ratio (ilr) transformations were utilized to open the geochemical data in order to reduce the effects of the data closure problem. The 20-quantile intervals decomposed anomaly maps from PC1 were obtained from the classical PCA, robust PCA showed that the upper quintile (>80 quintile) of classical PCA covers a larger area (32.54%) than the robust PCA (18.16%), and as a result, the robust PCA displayed smaller areas and has good spatial associations with outcrops of hydrothermal Au–As mineralization in this area; coincident with the known Zarshuran former mining area (ore field), Zarshuran unit, Ghaldagh silicified limestone occurrence and newly explored works confirmed by field observation. Although the C–A model shows a smaller area (8.06%), this anomaly location is limited to the Zarshuran old mining area with no new exploration targets. Comparison of the models indicates that the RPCA model is not only beneficial to further Au exploration in the study area, but also provides a meaningful geological study to the community of the compositional data analysis.     

Incorporation of principal component analysis,geostatistical interpolation approaches and frequency-space-based models for portraying the Cu-Au geochemical prospects in the Feizabad district,NW Iran

Delineation of mineralization-related geochemical anomalies of stream sediment data is an essential stage in regional geochemical exploration. In this study, principal component analysis (PCA) was applied to 12 selected elements to acquire a multi-element geochemical signature associated with Cu-Au mineralization in Feizabad district, NE Iran. The spatial distribution of enhanced multi-element geochemical signature of the second component (PC2) was modeled by different geostatistical procedures including variogram calculation, ordinary kriging (OK) and inverse distance weighting (IDW) interpolation techniques. Concentration-area (C-A) fractal and U-spatial statistics models were then applied to the continuous-value interpolated models for delineation of geochemical anomalies. Quantitative comparison of results based on the known mineral occurrences in the study area was carried out using normalized density index and success-rate curves. All generated models represent a high positive relation with known Cu (±Au) deposits in the study area, although, comparison of the results revealed that the OK-based U-spatial statistics model was superior to the rest of models. Besides, the low, moderate and high-intensity anomalies are spatially associated with geological-structural features in the study area.     

Pattern recognition techniques for evaluating the spatial impact of copper mining on surface waters

In this study, multivariate statistical approaches, namely hierarchical cluster analysis (CA) and principal component analysis (PCA), were employed to understand the impact of copper mining on surface waters located in Central-East India. The data set generated consisted of nine parameters, namely pH, dissolved oxygen (DO), alkalinity, total dissolved solids, copper, iron, manganese, zinc and fluoride, collected in forty sampling points covering all seasons. As delineated by CA, the entire data set for both the surface waters was bifurcated into groups, namely Banjar River inclusion of seepage points (BRISP) and Banjar River exclusion of seepage points (BRESP), Son River inclusion of seepage points (SRISP) and Son River exclusion of seepage points (SRESP). Four latent factors were identified, namely copper, iron, fluoride and manganese, explaining 84.7 % of variance for BRISP, 71.9 % of variance for BRESP, 66.7 % of variance for SRISP and 68 % of variance for SRESP. The extensive application of PCA on BRISP, BRESP, SRISP and SRESP reveals that the main stream of both the rivers remains unaffected by mining operations when seepage points were excluded. Additionally, iron content is considerably significant throughout the stream due to the geogenic sources and it is considered as a major factor for the depletion of DO level in the streams. This study reveals the level of contamination in the studied surface waters and the effectiveness of multivariate statistical techniques for evaluation and interpretation of complex data matrix in understanding the spatial variations and identification of pollution sources.