Sangam Shrestha

ASSESSMENT OF SURFACE WATER QUALITY USING MULTIVARIATE STATISTICAL TECHNIQUES: A CASE STUDY OF THE FUJI RIVER BASIN, JAPAN

Abstract Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were applied for the evaluation of temporal/spatial variations and the interpretation of a large complex water quality data set of the Fuji river basin, generated during 8 years (1995–2002) monitoring of 12 parameters at 13 different sites (14 976 observations). Hierarchical cluster analysis grouped 13 sampling sites into three clusters, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, based on the similarity of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the three different groups obtained from cluster analysis, resulted in five, five and three latent factors explaining 73.18, 77.61 and 65.39% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge and temperature (natural), organic pollution (point source: domestic wastewater) in relatively less polluted areas; organic pollution (point source: domestic wastewater) and nutrients (non-point sources: agriculture and orchard plantations) in medium polluted areas; and organic pollution and nutrients (point sources: domestic wastewater, wastewater treatment plants and industries) in highly polluted areas in the basin. Discriminant analysis gave the best results for both spatial and temporal analysis. It provided an important data reduction as it uses only six parameters (discharge, temperature, dissolved oxygen, biochemical oxygen demand, electrical conductivity and nitrate nitrogen), affording more than 85% correct assignations in temporal analysis, and seven parameters (discharge, temperature, biochemical oxygen demand, pH, electrical conductivity, nitrate nitrogen and ammonical nitrogen), affording more than 81% correct assignations in spatial analysis, of three different sampling sites of the basin. Therefore, DA allowed a reduction in the dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.

A framework for estimating pollutant export coefficients from long-term in-stream water quality monitoring data

Modeling techniques for estimating pollutant loadings to water bodies range from simple export coefficient and regression models to more complex mechanistic models. All export coefficient models and many complex mechanistic models rely on pollutant export coefficients to estimate pollution sources and transport in large watersheds. Typically, pollutant export coefficients are determined by monitoring small catchments or field plots to isolate individual landuse contributions. However, pollutant export coefficients derived from small catchment and field plot scale studies cannot be confidently used in catchment-scale water quality modeling. The objective of this paper is to present a framework to estimate the export coefficients of pollutants from commonly available in-stream water quality monitoring data. A combination of readily and freely available statistical, spatial and hydrological tools and a multiple regression methodology is proposed to estimate pollutant export coefficients. A case study from the Fuji River catchment, Japan is presented where export coefficients of organic matters and nutrients are estimated. Most of the estimated pollutant export coefficients are significant at @a equal to 0.05 and the landuse categories used in the multiple regression models explained more than 85% variability in loadings. These results are encouraging especially given the pressing need to identify appropriate management practices to improve the water quality within the catchment. It is recommended to investigate further the required number of water quality monitoring stations, sampling frequencies and sampling duration of water quality constituents to enhance the robustness and usefulness of the proposed methodology.

Assessment of groundwater vulnerability and risk to pollution in Kathmandu Valley, Nepal

Groundwater vulnerability and risk assessment is a useful tool for groundwater pollution prevention and control. In this study, GIS based DRASTIC model have been used to assess intrinsic aquifer vulnerability to pollution whereas Groundwater Risk Assessment Model (GRAM) was used to assess the risk to groundwater pollution in the groundwater basin of Kathmandu Valley. Seven hydrogeological factors were used in DRASTIC model to produce DRASTIC Index (DI) map which represent intrinsic groundwater vulnerability to pollution of the area. The seven hydrogeological factors used were depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity of aquifer. GIS based GRAM was used to produce likelihood of release of hazards, likelihood of detection of hazards, consequence of hazards and residual risk of groundwater contamination in terms of nitrate in the groundwater basin. It was found that more than 50% of the groundwater basin area in the valley is susceptible to groundwater pollution and these areas are mostly in Northern groundwater district Low and very low vulnerable areas account for only 13% and are located in Central and Southern groundwater districts. However after taking into account the barriers to groundwater pollution and likelihood of hazards release and detection, it was observed that most areas i.e. about 87% of the groundwater basin are at moderate residual risk to groundwater pollution. The resultant groundwater vulnerability and risk map provides a basis for policy makers and planners ability to use information effectively for decision making at protecting the groundwater from pollutants.

Vulnerability of freshwater resources in large and medium Nepalese river basins to environmental change.

This paper discusses vulnerability of freshwater resources in large and medium Nepalese river basins to environmental change based on evaluation of water resource availability and variation, resource development and use, ecological health and management capacity; and compares the situation with selected sub-basins of the Ganges and the Mekong basins in Asia. Results suggest that water resources in the medium river basins are more vulnerable than in the large basins; and Nepalese basins, in general, are more vulnerable than other selected basins in the Asian region. The vulnerability in the Nepalese basins is more related to poor management capacity followed by resources variation and the least to development pressure. The poor management capacity is mainly related to low productivity of water use and the resources stress is related mainly to variation of the resource.

Evaluation of index-overlay methods for groundwater vulnerability and risk assessment in Kathmandu Valley, Nepal

This study aimed at evaluating three index-overlay methods of vulnerability assessment (i.e., DRASTIC, GOD, and SI) for estimating risk to pollution of shallow groundwater aquifer in the Kathmandu Valley, Nepal. The Groundwater Risk Assessment Model (GRAM) model was used to compute the risk to groundwater pollution. Results showed that DRASTIC and SI methods are comparable for vulnerability assessment as both methods delineate around 80% of the groundwater basin area under high vulnerable zone. From the perspective of risk to pollution results, DRASTIC and GOD methods are comparable. Nevertheless, all the three methods estimate that at least 60% of the groundwater basin is under moderate risk to NO3-N pollution, which goes up to 75% if DRASTIC or GOD-based vulnerabilities are considered as exposure pathways. Finally, based on strength and significance of correlation between the estimated risk and observed NO3-N concentrations, it was found that SI method is a better-suited one to assess the vulnerability and risk to groundwater pollution in the study area. Findings from this study are useful to design strategies and actions aimed to prevent nitrate pollution in groundwater of Kathmandu Valley in Nepal.

Spatial distribution of arsenic in the intertidal sediments of River Scheldt, Belgium

A study was carried out to assess the spatial distribution of arsenic in the intertidal sediments of the River Scheldt in Belgium. Sediment samples were collected from different locations along the River Scheldt up to 100 cm depth and analysed for the major physicochemical properties. The study reveals that the arsenic contents in the sediment samples vary in a wide range, from 2.3 to 140.2 mg kg(-1) dry weight. Moreover, the arsenic concentrations are generally below the background concentrations and remediation thresholds of arsenic in Flanders, Belgium. The occurrence of arsenic is found closely related to some physicochemical properties of the sediments. Arsenic has a strong positive correlation with organic matter and clay contents. On the contrary, a negative correlation exists between arsenic, sand and pH. It is recommended to develop and use organic matter control practices for lowering further accumulation of arsenic within the sediments.

Evaluating the impacts of climate and land-use change on the hydrology and nutrient yield in a transboundary river basin: A case study in the 3S River Basin (Sekong, Sesan, and Srepok)

Assessment of the climate and land-use change impacts on the hydrology and water quality of a river basin is important for the development and management of water resources in the future. The objective of this study was to examine the impact of climate and land-use change on the hydrological regime and nutrient yield from the 3S River Basin (Sekong, Srepok, and Sesan) into the 3S River system in Southeast Asia. The 3S Rivers are important tributaries of the Lower Mekong River, accounting for 16% of its annual flow. This transboundary basin supports the livelihoods of nearly 3.5 million people in the countries of Laos, Vietnam, and Cambodia. To reach a better understanding of the process and fate of pollution (nutrient yield) as well as the hydrological regime, the Soil and Water Assessment Tool (SWAT) was used to simulate water quality and discharge in the 3S River Basin. Future scenarios were developed for three future periods: 2030s (2015-2039), 2060s (2045-2069), and 2090s (2075-2099), using an ensemble of five GCMs (General Circulation Model) simulations: (HadGEM2-AO, CanESM2, IPSL-CM5A-LR, CNRM-CM5, and MPI-ESM-MR), driven by the climate projection for RCPs (Representative Concentration Pathways): RCP4.5 (medium emission) and RCP8.5 (high emission) scenarios, and two land-use change scenarios. The results indicated that the climate in the study area would generally become warmer and wetter under both emission scenarios. Discharge and nutrient yield is predicted to increase in the wet season and decrease in the dry. Overall, the annual discharge and nutrient yield is projected to increase throughout the twenty-first century, suggesting sensitivity in the 3S River Basin to climate and land-use change. The results of this study can assist water resources managers and planners in developing water management strategies for uncertain climate change scenarios in the 3S River Basin.

A GIS-based methodology to delineate potential areas for groundwater development: a case study from Kathmandu Valley, Nepal

For an effective planning of activities aimed at recovering aquifer depletion and maintaining health of groundwater ecosystem, estimates of spatial distribution in groundwater storage volume would be useful. The estimated volume, if analyzed together with other hydrogeologic characteristics, may help delineate potential areas for groundwater development. This study proposes a GIS-based ARC model to delineate potential areas for groundwater development; where ‘A’ stands for groundwater availability, ‘R’ for groundwater release potential of soil matrix, and ‘C’ for cost for groundwater development. The model is illustrated with a case of the Kathmandu Valley in Central Nepal, where active discussions are going on to develop and implement groundwater management strategies. The study results show that shallow aquifers have high groundwater storage potential (compared to the deep) and favorable areas for groundwater development are concentrated at some particular areas in shallow and deep aquifers. The distribution of groundwater storage and potential areas for groundwater development are then mapped using GIS.

Green, Blue and Grey Water Footprints of Primary Crops Production in Nepal

This study aims to estimate the green, blue and grey water footprints (WFs) of nine primary crops production in 75 districts, 5 developmental regions and 3 physiographic divisions of Nepal using local meteorological, agronomical and irrigation data at high spatial resolution. The estimates are based on the framework prescribed by the guideline of the Water Footprint Network. The green and blue WFs are calculated using a water balance model whereas the grey WF is estimated as the volume of freshwater needed to dilute nitrate pollution to an acceptable level. WF varies across different crops considered, different districts, development regions and physiographic divisions. WF of potato and wheat in Nepal is comparable to the world average; but paddy, barley and pulses have higher while sugarcane and maize have lower values compared to the world average. WFs of paddy, maize, potato and wheat are lower in Terai than the Hills and Mountains due to the accessibility of irrigation system and higher crop yield. Millet, pulses, oilseeds and barley have lower WFs and are suitable for Mountains. Similarly, sugarcane is suitable for both Terai and Mountain divisions because of its lower WF. Crops in Far Western Development Region generally have higher WFs due to the low crop productivity, and higher fertilizer use.

Forecasting Climate Change Scenarios in the Bago River Basin, Myanmar

This study aims to forecast the climate change scenarios of Bago River Basin in Myanmar. A delta change method was used to correct the bias of maximum and minimum temperature and precipitation. The future projection period from 2010-2100 is classified into 2020s (2010-2039), 2050s (2040-2069), and 2080s (2070-2099) for analyzing meteorological parameters under RCP4.5 and RCP8.5 scenarios. It is observed that average annual maximum and minimum temperatures are projected to rise in the entire basin under both scenarios — most significantly in the 2080s. Average summer temperature is projected to decrease by approximately 0.25°C in the first century period under both RCPs. However winter season witnessed an increase in average temperature of 1.5-2.5°C, following by the rainy season with increase of average temperature of 0.9-2.6°C in future. Average annual precipitation shows a distinct increase in all three periods with the greatest upturn in the 2050s. Winter season is projected to receive more precipitation for both scenarios with an average increase of approximately 200 mm, whereas summer season shows the least rainfall change (25 mm) under both future scenarios. The highest mean monthly precipitation occurs in September during the 2020s (933 mm) and in July during the 2080s (868 mm) respectively. The average annual precipitation is projected to be at maximum in the 2020s (4085 mm, 40% increase) for RCP4.5 and in the 2050s (4263 mm, 43% increase) under RCP8.5.