Constructing robust and efficient experimental designs in groundwater modeling using a Galerkin method, proper orthogonal decomposition, and metaheuristic algorithms.

Estimating parameters accurately in groundwater models for aquifers is challenging because the models are non-explicit solutions of complex partial differential equations. Modern research methods, such as Monte Carlo methods and metaheuristic algorithms, for searching an efficient design to estimate model parameters require hundreds, if not thousands of model calls, making the computational cost prohibitive. One method to circumvent the problem and gain valuable insight on the behavior of groundwater is to first apply a Galerkin method and convert the system of partial differential equations governing the flow to a discrete problem and then use a Proper Orthogonal Decomposition to project the high-dimensional model space of the original groundwater model to create a reduced groundwater model with much lower dimensions. The reduced model can be solved several orders of magnitude faster than the full model and able to provide an accurate estimate of the full model. The task is still challenging because the optimization problem is non-convex, non-differentiable and there are continuous variables and integer-valued variables to optimize. Following convention, heuristic algorithms and a combination is used search to find efficient designs for the reduced groundwater model using various optimality criteria. The main goals are to introduce new design criteria and the concept of design efficiency for experimental design research in hydrology. The two criteria have good utility but interestingly, do not seem to have been implemented in hydrology. In addition, design efficiency is introduced. Design efficiency is a method to assess how robust a design is under a change of criteria. The latter is an important issue because the design criterion may be subjectively selected and it is well known that an optimal design can perform poorly under another criterion. It is thus desirable that the implemented design has relatively high efficiencies under a few criteria. As applications, two heuristic algorithms are used to find optimal designs for a small synthetic aquifer design problem and a design problem for a large-scale groundwater model and assess their robustness properties to other optimality criteria. The results show the proof of concept is workable for finding a more informed and efficient model-based design for a water resource study.

Multivariate statistics and water quality index (WQI) approach for geochemical assessment of groundwater quality-a case study of Kanavi Halla Sub-Basin, Belagavi, India.

Groundwater quality analysis has become essentially important in the present world scenario. In recent years, advanced technologies have replaced the traditional ones which are being helpful in simplifying the complex works. In this study, multivariate statistical analysis is carried out with the help of SPSS software for 45 groundwater samples of Kanavi Halla Sub-Basin (KHSB). The quality of groundwater is determined for various parameters which were analyzed and their concentration is correlated with other parameters using correlation matrix. The PCA technique is applied on water quality parameters, from which four components are extracted with 80.28% total variance. The extracted components suggest that the sources behind the higher loadings of each factor are by geological, agricultural, rainfall, domestic wastewater and industrial activities. Results of the Kaiser-Meyer-Olkin and Bartlett's test conducted have value of 0.659 which is greater than the standard value (0.5). Based on water quality index (WQI), it was noticeably depicted that 2/3rd of the KHSB groundwater quality falls under poor to very poor condition, and hardly 26% of groundwater available is portable. Thus, this study contributes the effective use of multivariate statistics and WQI analysis for groundwater quality. It helps in understanding the hydro-geochemistry of the groundwater and also aids in minimizing the larger set of data into smaller set with effective interpretation.

Quality criteria for groundwater use from a rural part of Wanaparthy District, Telangana State, India, through ionic spatial distribution (ISD), entropy water quality index (EWQI) and principal component analysis (PCA).

The present study region comprises granite and granite gneisses aquifer system constituted by Precambrian rocks. Groundwater is the primary source for drinking and other domestic purposes. Many developing regions in the world suffer from lack of safe drinking water. A rural part of Wanaparthy District in Telangana State, India, is one of them. For this reason, the groundwater samples collected from the study region were analyzed for pH, TDS, Ca2+, Mg2+, Na+, K+, HCO3-, Cl-, SO42-, NO3- and F- and evaluated groundwater quality criteria, using ionic spatial distribution (ISD), entropy water quality index (EWQI) and principal component analysis (PCA). The ISD maps show that some locations are not suitable for drinking purpose due to exceeding concentrations of TDS, Mg2+, Na+, K+, HCO3-, Cl-, NO3-and F-, compared to those with national drinking water quality standards. According to the EWQI, about 3%, 47%, 43% and 7% of the total area come under the excellent, good, medium and extremely poor water quality types for drinking purpose, respectively. Chadha's diagram classified the area as carbonate hardness (63%), non-carbonate alkali (17%), carbonates alkali (13%) and non-carbonate hardness (7%) zones. The binary diagrams (Na+ + K+ vs TC, Na+ vs Ca2+ and HCO3- vs TC) indicate that the quality of groundwater is controlled by influences of water-rock interactions, mineral weathering and dissolution, ion exchange and evaporation as well as the impact of anthropogenic sources. The PCA transferred the chemical variables into three principal components accounts for about 81% of the total variance. The high positive loadings of PC1 (Cl-, TDS, SO42-, Na+, NO3-, Mg2+ and HCO3-) stand for processes of silicate weathering and dissolution, ion exchange and evaporation, and the influence of domestic waste waters, irrigation return flows and chemical fertilizers on the groundwater system, the PC2 (F- and pH) signifies the alkaline nature of groundwater, which causes fluorosis, and the PC3 (K+) is a result of potassium fertilizers. The study helps to take remediate measures at a specific site and hence suggests the treatment of water before its drinking and also the recharge of the aquifer artificially to improve the groundwater quality.

Groundwater hydrogeochemical formation and evolution in a karst aquifer system affected by anthropogenic impacts.

Karst groundwater, an important water source, is often highly influenced by human impacts, causing environmental damage and threats to human health. However, studies on the anthropogenic influences on the hydrogeochemical evolution of karst groundwater are relatively rare. To assess hydrogeochemical formation and evolution, we focused on a typical karst groundwater system (Jinan, China) which is composed of cold groundwater (av. temperature 13-17 °C), springs and geothermal water (av. temperature > 30 °C) and is significantly affected by human activities. The study was performed by means of water samples collecting and analyzing and isotope analysis (2H, 18O and 14C). The statistical analysis and inverse models were also applied to further understand geochemical processes and anthropogenic influences. The 2H, 18O and 14C results indicate that the cold karst groundwater is easily influenced and contaminated by the local environment, while geothermal water is relatively old with a slow rate of recharge. The hydrochemical types of cold karst groundwater are mainly HCO3-Ca and HCO3·SO4-Ca, while geothermal water hydrochemical types are SO4-Ca·Na and SO4-Ca. Groundwater Ca2+, Mg2+, HCO3- and SO42- are mainly controlled by carbonate equilibrium, gypsum dissolution and dedolomitization. Groundwater Na+, K+ and Cl- are mainly derived from halite dissolution, and in geothermal water, they are also affected by incongruent dissolution of albite and K-feldspar. Anthropogenic nitrogen produces ammonium resulting in nitrification and reduction in CO2(g) consumption and HCO3- release from carbonate dissolution. Principal component analysis and inverse models also indicate that nitrification and denitrification have significantly affected water-rock interactions. Our study suggests that karst groundwater quality is dominated by water-rock interactions and elucidates the influence of anthropogenic nitrogen. We believe that this paper will be a good reference point to study anthropogenic influences on the groundwater environment and to protect karst groundwater globally.

The impact of the Gabcíkovo hydroelectric power barrier on the Danube floodplain environment-the results of long-term monitoring of land snail fauna.

The Gabcíkovo Water Project, a major construction of damming and canalizing on the upper part of the middle Danube, significantly changed hydrological regime of the Danube inland delta, destroyed or affected most of the 230 km2 of wetlands and directly disrupted the original and unique ecosystem along a 37 km long river stretch. The aim of this study was to describe the effect of the Gabcíkovo Waterworks on the taxonomical and functional structure of the molluscan assemblages in the Danube riparian floodplain forests. The results demonstrate that the Gabcíkovo Waterworks had a direct and long-lasting effect on the direction of the succession of terrestrial molluscan assemblages, especially in the area of the by-pass section. The changes in the soil moisture caused by the waterworks' operation led to significant changes in the species and functional composition of these assemblages. More specifically, however, the proportion of the generalists which prefer dry biotopes increased, while the number of moisture-demanding species decreased. Our results indicate that the current artificial flooding system cannot fully replace previous natural floods in the Danube inland delta, and it is also insufficient for restoration and preservation of the humidity conditions in the softwood floodplain forests which would be similar to the pre-operation period of the Gabcíkovo Waterworks.

The 2011 National Wetland Condition Assessment: overview and an invitation.

The first National Wetland Condition Assessment (NWCA) was conducted in 2011 by the US Environmental Protection Agency (USEPA) and its federal and state partners, using a survey design that allowed inference of results to national and regional scales. Vegetation, algae, soil, water chemistry, and hydrologic data were collected at each of 1138 locations across the conterminous United States (US). Ecological condition was assessed in relation to a disturbance gradient anchored by least disturbed (reference) and most disturbed sites identified using chemical, physical, and biological disturbance indices based on site-level data. A vegetation multimetric index (VMMI) was developed as an indicator of condition, and included four metrics: a floristic quality assessment index, relative importance of native plants, number of disturbance-tolerant plant species, and relative cover of native monocots. Potential stressors to wetland condition were identified and incorporated into two indicators of vegetation alteration, four indicators of hydrologic alteration, a soil heavy metal index, and a nonnative plant indicator and were used to quantify national and regional stressor extent, and the associated relative and attributable risk. Approximately 48 ± 6% of the national wetland area was found to be in good condition and 32 ± 6% in poor condition as defined by the VMMI. Across the conterminous US, approximately 20% of wetland area had high or very high stressor levels related to nonnative plants. Vegetation removal, hardening, and ditching stressors had the greatest extent of wetland area with high stressor levels, affecting 23-27% of the wetland area in the NWCA sampled population. The results from the 2016 NWCA will build on those from the 2011 assessment and initiate the ability to report on trends in addition to status. The data and tools produced by the NWCA can be used by others to further our knowledge of wetlands in the conterminous US.

RiverCore: IoT Device for River Water Level Monitoring over Cellular Communications.

Flooding is one of the most frequent and costly natural disasters affecting mankind. However, implementing Internet of Things (IoT) technology to monitor river behavior may help mitigate or prevent future disasters. This article outlines the hardware development of an IoT system (RiverCore) and defines an application scenario in a specific hydrological region of the state of Colima (Mexico), highlighting the characteristics of data acquisition and data processing used. Both fixed position and moving drifter node systems are described along with web-based data acquisition platform developments integrated with IoT techniques to retrieve data through 3G cellular networks. The developed architecture uses the Message Queuing Telemetry Transport (MQTT) protocol, along with encryption and security mechanisms, to send real-time data packages from fixed nodes to a server that stores retrieved data in a non-relational database. From this, data can be accessed and displayed through different customizable queries and graphical representations, allowing future use in flood analysis and prediction systems. All of these features are presented along with graphical evidence of the deployment of the different devices and of several cellular communication and on-site data acquisition tests.

An efficient multi-objective optimization method for water quality sensor placement within water distribution systems considering contamination probability variations.

Water quality security within water distribution systems (WDSs) has been an important issue due to their inherent vulnerability associated with contamination intrusion. This motivates intensive studies to identify optimal water quality sensor placement (WQSP) strategies, aimed to timely/effectively detect (un)intentional intrusion events. However, these available WQSP optimization methods have consistently presumed that each WDS node has an equal contamination probability. While being simple in implementation, this assumption may do not conform to the fact that the nodal contamination probability may be significantly regionally varied owing to variations in population density and user properties. Furthermore, the low computational efficiency is another important factor that has seriously hampered the practical applications of the currently available WQSP optimization approaches. To address these two issues, this paper proposes an efficient multi-objective WQSP optimization method to explicitly account for contamination probability variations. Four different contamination probability functions (CPFs) are proposed to represent the potential variations of nodal contamination probabilities within the WDS. Two real-world WDSs are used to demonstrate the utility of the proposed method. Results show that WQSP strategies can be significantly affected by the choice of the CPF. For example, when the proposed method is applied to the large case study with the CPF accounting for user properties, the event detection probabilities of the resultant solutions are approximately 65%, while these values are around 25% for the traditional approach, and such design solutions are achieved approximately 10,000 times faster than the traditional method. This paper provides an alternative method to identify optimal WQSP solutions for the WDS, and also builds knowledge regarding the impacts of different CPFs on sensor deployments.

Climatic control of Mississippi River flood hazard amplified by river engineering.

Over the past century, many of the world's major rivers have been modified for the purposes of flood mitigation, power generation and commercial navigation. Engineering modifications to the Mississippi River system have altered the river's sediment levels and channel morphology, but the influence of these modifications on flood hazard is debated. Detecting and attributing changes in river discharge is challenging because instrumental streamflow records are often too short to evaluate the range of natural hydrological variability before the establishment of flood mitigation infrastructure. Here we show that multi-decadal trends of flood hazard on the lower Mississippi River are strongly modulated by dynamical modes of climate variability, particularly the El Niño-Southern Oscillation and the Atlantic Multidecadal Oscillation, but that the artificial channelization (confinement to a straightened channel) has greatly amplified flood magnitudes over the past century. Our results, based on a multi-proxy reconstruction of flood frequency and magnitude spanning the past 500 years, reveal that the magnitude of the 100-year flood (a flood with a 1 per cent chance of being exceeded in any year) has increased by 20 per cent over those five centuries, with about 75 per cent of this increase attributed to river engineering. We conclude that the interaction of human alterations to the Mississippi River system with dynamical modes of climate variability has elevated the current flood hazard to levels that are unprecedented within the past five centuries.

The uncertainty of biodegradation rate constants of emerging organic compounds in soil and groundwater - A compilation of literature values for 82 substances.

The present study reports on biodegradation rate constants of emerging organic compounds (EOCs) in soil and groundwater available in the literature. The major aim of this compilation was to provide an assessment of the uncertainty of hydrological models with respect to the fate of EOCs. The literature search identified a total number of 82 EOCs for which 1st-order rate constants could be derived. It was found that for the majority of compounds degradation rate constants vary over more than three orders of magnitude. Correlation to factors that are well known to affect the degradation rate, such as temperature or redox condition was weak. No correlation at all was found with results from available quantitative structure-activity relationship models. This suggests that many unknown site specific or experimentally specific factors influence the degradation behavior of EOCs in the environment. Thus, local and catchment scale predictive models to estimate EOC concentration at receptors, e.g., receiving waters or drinking water wells, need to consider the large uncertainty in 1st-order rate constants. As a consequence, applying rate constants that were derived from one experiment or field site investigation to other experiments or field sites should be done with extreme caution.

Climate Change Impacts on the Upper Indus Hydrology: Sources, Shifts and Extremes.

The Indus basin heavily depends on its upstream mountainous part for the downstream supply of water while downstream demands are high. Since downstream demands will likely continue to increase, accurate hydrological projections for the future supply are important. We use an ensemble of statistically downscaled CMIP5 General Circulation Model outputs for RCP4.5 and RCP8.5 to force a cryospheric-hydrological model and generate transient hydrological projections for the entire 21st century for the upper Indus basin. Three methodological advances are introduced: (i) A new precipitation dataset that corrects for the underestimation of high-altitude precipitation is used. (ii) The model is calibrated using data on river runoff, snow cover and geodetic glacier mass balance. (iii) An advanced statistical downscaling technique is used that accounts for changes in precipitation extremes. The analysis of the results focuses on changes in sources of runoff, seasonality and hydrological extremes. We conclude that the future of the upper Indus basin's water availability is highly uncertain in the long run, mainly due to the large spread in the future precipitation projections. Despite large uncertainties in the future climate and long-term water availability, basin-wide patterns and trends of seasonal shifts in water availability are consistent across climate change scenarios. Most prominent is the attenuation of the annual hydrograph and shift from summer peak flow towards the other seasons for most ensemble members. In addition there are distinct spatial patterns in the response that relate to monsoon influence and the importance of meltwater. Analysis of future hydrological extremes reveals that increases in intensity and frequency of extreme discharges are very likely for most of the upper Indus basin and most ensemble members.

Research on classified real-time flood forecasting framework based on K-means cluster and rough set.

This research presents a new classified real-time flood forecasting framework. In this framework, historical floods are classified by a K-means cluster according to the spatial and temporal distribution of precipitation, the time variance of precipitation intensity and other hydrological factors. Based on the classified results, a rough set is used to extract the identification rules for real-time flood forecasting. Then, the parameters of different categories within the conceptual hydrological model are calibrated using a genetic algorithm. In real-time forecasting, the corresponding category of parameters is selected for flood forecasting according to the obtained flood information. This research tests the new classified framework on Guanyinge Reservoir and compares the framework with the traditional flood forecasting method. It finds that the performance of the new classified framework is significantly better in terms of accuracy. Furthermore, the framework can be considered in a catchment with fewer historical floods.

Hydrological modelling for siberian crane Grus Leucogeranus stopover sites in northeast China.

Habitat loss is one of the key factors underlying the decline of many waterbird species, including Siberian Crane (Grus leucogeranus), a threatened species worldwide. Wetlands are the primary stopover for many waterbirds and restoration of these wetlands involves both hydrological restoration and water resource management. To protect the stopover sites of Siberian Cranes, we collected Siberian Crane stopover numbers, meteorological and hydrological data, and remote sensing data from 2008 to 2011 in Momoge National Nature Reserve, one of the largest wetlands in northeastern China. A model was developed to estimate the suitability of Siberian Crane stopover sites. According to our results, the most suitable daily water level for Siberian Cranes between 2008 and 2012 occurred in the spring of 2008 and in the Scirpus planiculmis growing season and autumn of 2010. We suggest a season-dependent water management strategy in order to provide suitable conditions at Siberian Crane stopover sites.

Estimating daily time series of streamflow using hydrological model calibrated based on satellite observations of river water surface width: Toward real world applications.

Lacking observation data for calibration constrains applications of hydrological models to estimate daily time series of streamflow. Recent improvements in remote sensing enable detection of river water-surface width from satellite observations, making possible the tracking of streamflow from space. In this study, a method calibrating hydrological models using river width derived from remote sensing is demonstrated through application to the ungauged Irrawaddy Basin in Myanmar. Generalized likelihood uncertainty estimation (GLUE) is selected as a tool for automatic calibration and uncertainty analysis. Of 50,000 randomly generated parameter sets, 997 are identified as behavioral, based on comparing model simulation with satellite observations. The uncertainty band of streamflow simulation can span most of 10-year average monthly observed streamflow for moderate and high flow conditions. Nash-Sutcliffe efficiency is 95.7% for the simulated streamflow at the 50% quantile. These results indicate that application to the target basin is generally successful. Beyond evaluating the method in a basin lacking streamflow data, difficulties and possible solutions for applications in the real world are addressed to promote future use of the proposed method in more ungauged basins.

A two-dimensional coupled flow-mass transport model based on an improved unstructured finite volume algorithm.

A two-dimensional coupled water quality model is developed for modeling the flow-mass transport in shallow water. To simulate shallow flows on complex topography with wetting and drying, an unstructured grid, well-balanced, finite volume algorithm is proposed for numerical resolution of a modified formulation of two-dimensional shallow water equations. The slope-limited linear reconstruction method is used to achieve second-order accuracy in space. The algorithm adopts a HLLC-based integrated solver to compute the flow and mass transport fluxes simultaneously, and uses Hancock's predictor-corrector scheme for efficient time stepping as well as second-order temporal accuracy. The continuity and momentum equations are updated in both wet and dry cells. A new hybrid method, which can preserve the well-balanced property of the algorithm for simulations involving flooding and recession, is proposed for bed slope terms approximation. The effectiveness and robustness of the proposed algorithm are validated by the reasonable good agreement between numerical and reference results of several benchmark test cases. Results show that the proposed coupled flow-mass transport model can simulate complex flows and mass transport in shallow water.

Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.

Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for the effective reservoir management. In this research, an artificial neural network (ANN) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting medium and long-term runoff time series. First, the original runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and a residual series using EEMD technique for attaining deeper insight into the data characteristics. Then all IMF components and residue are predicted, respectively, through appropriate ANN models. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Two annual reservoir runoff time series from Biuliuhe and Mopanshan in China, are investigated using the developed model based on four performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and the proposed EEMD-ANN model can attain significant improvement over ANN approach in medium and long-term runoff time series forecasting.

Analysis of temporal and spatial trends of hydro-climatic variables in the Wei River Basin.

The Wei River is the largest tributary of the Yellow River in China. The relationship between runoff and precipitation in the Wei River Basin has been changed due to the changing climate and increasingly intensified human activities. In this paper, we determine abrupt changes in hydro-climatic variables and identify the main driving factors for the changes in the Wei River Basin. The nature of the changes is analysed based on data collected at twenty-one weather stations and five hydrological stations in the period of 1960-2010. The sequential Mann-Kendall test analysis is used to capture temporal trends and abrupt changes in the five sub-catchments of the Wei River Basin. A non-parametric trend test at the basin scale for annual data shows a decreasing trend of precipitation and runoff over the past fifty-one years. The temperature exhibits an increase trend in the entire period. The potential evaporation was calculated based on the Penman-Monteith equation, presenting an increasing trend of evaporation since 1990. The stations with a significant decreasing trend in annual runoff mainly are located in the west of the Wei River primarily interfered by human activities. Regression analysis indicates that human activity was possibly the main cause of the decline of runoff after 1970.

Improving calibration of two key parameters in Hydrologic Engineering Center hydrologic modelling system, and analysing the influence of initial loss on flood peak flows.

Parameter calibration is a key and difficult issue for a hydrological model. Taking the Jinjiang Xixi watershed of south-east China as the study area, we proposed methods to improve the calibration of two very sensitive parameters, Muskingum K and initial loss, in the Hydrologic Engineering Center hydrologic modelling system (HEC-HMS) model. Twenty-three rainstorm flood events occurring from 1972 to 1977 were used to calibrate the model using a trial-and-error approach, and a relationship between initial loss and initial discharge for these flood events was established; seven rainstorm events occurring from 1978 to 1979 were used to validate the two parameters. The influence of initial loss change on different return-period floods was evaluated. A fixed Muskingum K value, which was calibrated by assuming a flow wave velocity at 3 m/s, could be used to simulate a flood hydrograph, and the empirical power-function relationship between initial loss and initial discharge made the model more applicable for flood forecasting. The influence of initial loss on peak floods was significant but not identical for different flood levels, and the change rate of peak floods caused by the same initial loss change was more remarkable when the return period increased.

Model simulation of inflow water to the Baltic Sea based on ¹²9I.

The semi-enclosed Baltic Sea represents a vital economic and recreational resource for more than 90 million people inhabiting its coasts. Extensive contamination of this sea by a variety of anthropogenic pollutants has raised the concern of the people in the region. Quantifying seawater inflow is crucial for estimating potential environmental risks as well as to find the best remedial strategy. We present here a model to estimate water inflow from the North Sea to the Baltic Sea by utilizing ¹²9I as a tracer. The results predicted inflow range of 230-450 km³/y with best fit value around 330 km³/y from the North Sea to the Baltic Sea during 1980-1999. Despite limited time series data on ¹²9I, the model presented here demonstrates a new management tool for the Baltic Sea to calculate inflow water compared to conventional methods (such as salinity, temperature and hydrographic models).

Environmental characteristics of anopheline mosquito larval habitats in a malaria endemic area in Iran.

OBJECTIVE: To determine the effects of environmental parameters of larval habitats on distribution and abundance of anopheline mosquitoes in Rudan county of Iran. METHODS: This cross-sectional study was conducted during the mosquito breeding season from February 2010 to October 2011. The anopheline larvae were collected using the standard dipping method. The specimens were identified using a morphological-based key. Simultaneously with larval collection, environmental parameters of the larval habitats including water current and turbidity, sunlight situation, and substrate type of habitats were recorded. Water samples were taken from breeding sites during larval collection. Before collection of samples, the water temperature was measured. The water samples were analysed for turbidity, conductivity, total alkalinity, total dissolved solid, pH and ions including chloride, sulphate, calcium, and magnesium. Statistical correlation analysis and ANOVA test were used to analyze the association between environmental parameters and larval mosquito abundance. RESULTS: In total 2 973 larvae of the genus Anopheles were collected from 25 larval habitats and identified using morphological characters. They comprised of six species: An. dthali (53.21%), An. stephensi (24.22%), An. culicifacies (14.06%), An. superpictus (4.07%), An. turkhudi (3.30%), and An. apoci (1.14%). The most abundant species was An. dthali which were collected from all of the study areas. Larvae of two malaria vectors, An. dthali and An. stephensi, co-existed and collected in a wide range of habitats with different physico-chemical parameters. The most common larval habitats were man-made sites such as sand mining pools with clean and still water. The anopheline mosquitoes also preferred permanent habitats in sunlight with sandy substrates. The results indicated that there was a significant relationship between mean physico-chemical parameters such as water temperature, conductivity, total alkalinity, sulphate, chloride, and mosquito distribution and abundance. CONCLUSIONS: The results of this study showed a correlation between certain environmental parameters and mosquito larvae abundance, and these parameters should be considered in planning and implementing larval control programs.