III. The approach has shown that catchment specific data is appropriate in under- standing the dynamics of pollution behaviour in agreement with what NOAA Coastal Services Center (2004); Line et al. (2002); Oki (2003); and City and County of Honolulu (2007) have found out in similar studies. The summary of outcomes from the study undertaken include; 1) the development of a waterquality profile for Kuils-EersteRivercatchment for an overall evaluation of the catchment and the different land-covers that contribute to the pollutant loading; 2) the identification of the various sources of contamination to allow for the implementation of appropriate management strategies; 3) the identifi- cation of sources of pollution which cause and sustain poor waterquality in the catchment; and 4) the development of a critical nonpoint monitoring pro- gramme for the Cape Town Metropolitan Authorities for the monitoring, management and mitigation of pollutant inputs in the catchment. The Event Mean Concentration (EMC) was derived as the flow-weighted mean concen- tration of contaminant. Individual storm EMC values were then summarised as either the arithmetic mean, the flow-weighted mean (total load from storm events divided by total discharge volume), or the median of event EMCs. Since the assessment of urban overland flow quality of Kuils-Eersterivercatchment required a consideration of different types of land-cover, it was rep- resented by the event mean concentration (EMC) value. The EMC determined, represent the concentration of a specific pollutant contained coming from a particular land-cover type within the catchment. The aim of the study was ful- filled which sought to explain how the quality of surface runoff varied on dif-
Water is basic to individuals and the biggest accessible wellspring of crisp water lies underground. Expanded requests for water have animated investigation of underground water assets. Water assets get contaminated because of fast industrialization, headway in agrarian methods, expanding populace and other unfavourable effects of situations. Every one of these elements may bring about changing the hydrological cycle. The urban natural quality dependably relies upon the utilization of land. The nature of the earth is controlled by concentrate the land utilize highlights and their effects are investigated. In the present investigation, an endeavour is had to assess the effect of landuse/landcover on groundwater nature of Zone VII under the Greater Hyderabad Municipal Corporation (GHMC) zone. Different topical maps are set up from the toposheet on 1:50000 scale utilizing ArcGIS Software. The land-use/landcover guide of the investigation region is set up from the straightly improved melded information of IRS-1D PAN and LISS-III satellite symbolism by utilizing Visual Interpretation Techniques. Groundwater tests were haphazardly gathered at pre-decided inspecting areas dependent on satellite symbolism of the investigation zone. Every one of the examples was broken down for different physical-synthetic parameters embracing standard conventions for the age of trait information. In view of the outcomes got maps demonstrating spatial circulation of chose waterquality parameters is set up for the examination region. The varieties in the groupings of waterquality parameters showed high convergences of Alkalinity, TDS, Fluoride, Hardness, Nitrates are surpassed as far as possible while different parameters like Sodium, Sulfate and Chloride were inside as far as possible aside from in a couple of zones like Golnaka, Imlibun, Kamalanagar and so forth., which might be ascribed to leakage of residential squanders through open nallahs and modern squanders. The waterquality file (WQI) in the examination region is computed to decide the appropriateness of groundwater for drinking reason. Diverse appraisals of waterquality have been seen which showed falling apart nature of groundwater. Control and therapeutic measures for the change of groundwater quality in the examination zone are proposed.
1 A Ekulu River at New market fly over 6°28'2.97"N 7°28'23.477"E 2 B Ekulu River at ujodo Development Centre 6°28'9.368"N 7°29'7.311"E 3 C Ekulu River at Abakpa 1st Bus stop 6°28'36.045"N 7°31'8.772"E 4 D Asata River at Enugu Port-Harcourt Express way, New Artisan 6°27'17.934"N 7°32'24.803"E 5 E Asata River at Amigbo Lane CIC. 6°27'20.15"N 7°30'44.56"E 6 F Aria River at Works Road 6°26'53.509"N 7°28'55.007"E 7 G Aria River at Access Bank , Garden Avenue 6°27'11.184"N 7°29'38.807"E 8 H Idaw River at Maryland, Timber Shed 6°24'52.165"N 7°30'11.164"E 9 I Idaw River at Abalukwu Street 6°24'57.079"N 7°29'37.323"E 10 J Ayo river at Enugu Port Harcourt Express way, Ugwuaji Bridge 6°24'42.435"N 7°31'53.299"E 11 K Ogbete River at Old UNTH 6°26'2.095"N 7°28'45.137"E 12 L Ogbete River at Holy Ghost Cathedral 6°26'0.321"N 7°29'20.849"E 13 M Ayo River at Amechi Road 6°23'58.858"N 7°29'56.286"E
7 CHAPTER 2
There are many catchment areas in Malaysia now under pressure from urban, industrial and development of high class of infrastructures since we are moving towards 2020 Vision. As a result, the downstream receiving water bodies such as lakes, rivers, ponds, reservoirs, estuary and costal waters have become more and more sensitive where it’s rates and volumes of runoff increased and the same goes to the pollutant discharged to the water bodies. Many urban and residential areas especially in the Western States of Peninsular Malaysia like Perak Darul Ridzuan are experiencing the effects of these problems. The problems get worst when there are frequent intense rainfalls, the physiological characteristics of the basin as well as the pattern of urbanization are very bad in urban areas (DID, 1994).
27 The effects of human activities resulting in degradation of environmental characteristics of streams are the main cause of alteration structures and functions of aquatic biota leading to the need for waterquality assessment. Certain benthic macroinvertebrates recognized as sensitive to perturbation in their environment and habitat characteristics, have been widely considered as best biological indicators (Stoyanova et al. 2010; Ngera et al. 2009; Makoba et al., 2008; Sundermann et al. 2008; Arimoro et al. 2007; Dallas, 2007; Duran, 2006; Hauer and Lamberti, 2006; Chapman and Chapman, 2002; Abel, 2002; Mason, 2002; Davies and Day, 1998; Olomukoro and Ezemonye, 2007). These organisms reflect the intensity of anthropogenic stress and respond to the totality of environmental conditions which they have experienced throughout their lives. Their responses to environmental conditions usually depend on the nature and severity of the pollution (Abel, 2002). The presence of certain species such as mayflies (Ephemeroptera), caddisflies (Trichoptera), and stoneflies (Plecoptera) often indicates that the water is well oxygenated although their absence does not necessarily indicate the converse (Stoyanova et al. 2010; Lorion and Kennedy, 2009; Robertson, 2006) whereas the dominance of aquatic worms, chironomids, leeches and pouch snails usually signifies poor waterquality (Robertson, 2006; Fisher, 2003; Abel, 2002). Following their response to organic or inorganic pollutants (Duran, 2006), diverse biotic indices were developed to evaluate the waterquality in rivers (Chutter, 1972; Chapman, 1996; Abel, 2002; Duran, 2006). In this regard, Kolkwitz and Marsson (1902 and 1909) cited by Abel, (2002) and Chapman (1996), set the pace in Europe to explore the response of macroinvertebrates using the Saprobic System. Currently, over 100 different biotic indices have been developed throughout the world (Ziglio et al., 2006). The South African Scoring System (SASS) based on the British Biological Monitoring Working Party (BBMWP) method has been initiated and adapted for South African conditions originally by Dr F. M. Chutter in 1994 (Davies and Day, 1998; Dallas, 2000 ; Dickens and Graham, 2002).
Landuse practices can adversely affect waterquality and freshwater mussel populations. Waterquality can become degraded by siltation from development, pesticides and nutrients from agricultural fields, heavy metals and other toxins from urban runoff. The relationship between landuse/landcover and freshwater mussel populations was investigated in the upper Neuse River basin in North Carolina. Mussel surveys were conducted from April to August of 2001 in the Eno, Flat, Smith, New Light, and Little River watersheds. Surveys (n=44) were conducted along 300-m transects upstream and downstream of bridges to examine the effect of bridge crossing structures on mussel assemblages. Geographic Information Systems (GIS) hydrological modeling tools were used to delineate upstream catchments of each sample site and to determine drainage areas. GIS was used to quantify landuse/landcoverwithin multiple spatial areas: upstream catchment, upstream riparian buffers (100 m and 250 m widths), and local riparian buffers (100 m and 250 m widths) immediate to the sample sites. Other environmental variables included stream slope, road density, water chemistry, and habitat quality assessment scores.
Remote sensing and GIS are efficient aids in preparing and analyzing spatial datasets such as satellite data, digital elevation models (DEMs), etc. Remote sensing technology is used in preparing LULC maps of a region, whereas GIS helps in the delineation of river basin boundaries, extraction of the study area, hydrological modeling, spatiotemporal data analysis, etc. (Kindu et al., 2015; Kumar and Jhariya, 2015; Wilson, 2015). The selection of an appropriate method for a study is based on the objectives and availability of the data and tools required for the study. Ban et al. (2014) observed that waterquality monitoring programs monitor and pro- duce large and complex waterquality datasets. Waterquality trends vary both spatially and temporally, causing difficulty in establishing a relationship between waterquality parame- ters and LULC changes (Phung et al., 2015; Russell, 2015). Assessment of surface waterquality of a river basin can be carried out using various waterquality and pollution indices based on environmental standards (Rai et al., 2011). These indices are the simplest and fastest indicators to evaluate the status of waterquality in a river (Hoseinzadeh et al., 2014). Demographic growth, LULC changes and their effects on waterquality in a region are very site specific. Hence, dif- ferent regions and countries have developed their own waterquality and pollution indices for different types of water uses based on their respective waterquality standards and permis- sible pollution limits (Abbasi and Abbasi, 2012; Rangeti et al., 2015).
To gain an understanding of landcover change and use over time and space, aerial photographs were mapped from 1980 to 2013. The historical analysis provided an initial context for waterquality comparison in each catchment since waterquality response to the historical pattern of landuse change and catchment impacts can be immediate or have a lag effect. Digital, orthorectified aerial photographs for 2006, 2010 and 2013 were available. The earliest photographs were orthorectified to a common scale and mosaiced using a digital elevation model as a base layer for topographical data. The same area was assessed and digitised on-screen using ArcMap 10.2 (ESRI) and the resulting maps were overlain to observe changes in landcover to give a total estimate of the amount of change for the mapped categories. The mapping boundaries used were sub-catchments delineated by Maherry et al. (2013). The percentage for each landcover category was calculated for the sub-catchment and a buffer zone along each bank of the rivers. A buffer zone of approximately 100 m was used based on the lowest resolution of the digital data and catchment topography.
Estuaries, being the interface between the rivers, land and sea are largely influenced by what the rivers bring in via runoff from upstream in the catchment. Turpie et al. (2002) state that the management and the quantity and quality of freshwater inputs will directly affect the future health and productivity of estuaries in South Africa. For this reason the study focused on waterquality and landcover further upstream, as this is the water that will ultimately affect the estuaries. Using data from further upstream also reduces the effect of internal processes such as evaporation and internal loading/sequestration (Bowes & House 2001) which could mask the effect of landcover on waterquality. If only the estuary were to be considered in terms of waterquality, pollution sources near the estuary could be disguised by floodwaters from further upstream that have a diluting effect. In areas with distinct seasonal rainfall and flow peaks the waterquality variables measured in streams and rivers could be in direct contrast to those measured in the estuary, depending on the season, amount of flow and discharge pulses. Salts may accumulate upstream in the rivers during drier periods while the estuary remains unaffected, or floods may bring an accumulation of salts to the estuary even if upstream the dynamics and values are quite different. Focusing on the rivers eliminated these variables and made comparisons with landcover easier. Also by studying the effects in sub-catchments as opposed to the catchment as a whole, more direct comparisons and deductions could be made by identifying more localised changes and their direct effect on the waterquality measured in the area.
In this study the majority of the literature was derived from North American and European studies although there are significant climatic and environmental differences. But these studies do provide ideas how the riverwaterquality can deteriorate and why. In addition there were few published studies available for Australian conditions related to the landuse affects on waterquality at a catchment scale or on a riparian zone basis. For example studies conducted in Rous Rivercatchment in northern NSW Australia showed that elevated levels of nutrients were associated with leaching of excess fertiliser that have been applied in cane land (Eyre & Pepperell 1999). Ierodiacanou et al. (2005) studied a regional scale assessment of landuse change on nutrient exports by using an export coefficient model, remote sensing and GIS technique in south west Victoria. During period of 1980 to 2002 the modelled phosphorus and nitrogen loads were increased by 0.14 kg/ha and 1.37 kg/ha respectively when landuse changed from dryland pasture to more intensive agricultural activities such as cropping and irrigated pasture. Similarly, empirical studies have been done on the significant contribution of agricultural landuse (Nash et al. 2004; Webster et al. 2001) and managed pasture land (Nash & Halliwell 2000; Fleming & Cox 2001) to excessive phosphorus concentration in the waterways of South Australia. However, some review papers related to landuse and nutrient export in river systems have been written from an Australian prospective but these often use northern hemisphere data due to lack of relevant long-term Australian data sets (Young et al. 1996).
The Mann-Kendall test and Sen’s Slope estimates were applied employed to the entire BSR to determine the LLC trends in the watershed, and to all the gauging stations within BSR to determine the nitrate trends in each station. With a drainage area of approximately 9,000 square miles and connections to several smaller tributaries, it would be difficult to reliably estimate actual changes occurring within the area near any BSR gauging station. Therefore, estimates for the HUC12 catchments were done. Tomer et al. (2013) suggested the use of the HUC12 catchment for a more detailed analysis at a localized level because these catchments also account for tributaries. Moreover, Tobler’s first law of Geography suggests that “Everything is related to everything else. But near things are more related than distant things” (Tobler 1969).
The simulation results in this study shows that significant changes in the stream flow in the Nyangores River have occurred. The simulated stream flow hydrographs shows a higher flow peaks for the 2010 land-usecover datasets than the 1995 land-usecover datasets. The monthly stream flow reveal that the discharge at the river gauging station have increased during the study period. The shift in peak flows between the study periods indicates the potential effects of LULC on the stream flow in the Nyangores River. The high peak flow in 2010 datasets indicates that for every rainfall event in the sub-catchment, rain water flows faster as surface runoff from the catchment to the stream. Such scenarios indicate that water interception in the sub-catchment is low and therefore less time for infiltration. This is caused by a decrease in Forest cover, decrease in Tree plantation and increase in Farmlands therefore reducing rain water interceptions leading to increase in surface run offs in the sub-catchment (Olang’, 2009)
The average rainfall in the basin is about 700 mm. The river flows from west to east and its maximum elevation is around 1600 m above mean sea level and the minimum elevation is 100 m above mean sea level . It is also believed that water contains natural medicine and therefore it is good for health. Noyyal is a seasonal river which has good flow only for short periods during the north- east and southwest monsoons. Occasionally flash floods occur when there is heavy rain in the catchment areas . Apart from these periods, there is only scanty flow in most part of the year. Generally, a subtropical climate condition prevails in the river basin. It is divided into, winter from January to February, summer from March to May and it is followed by southwest monsoon from June to September and from October to December constituting the postmonsoon sea- son . The rainfall in western parts is comparatively more during the southwest monsoon season while the eastern parts get more rainfall during the northeast monsoon season . The precipitation is unevenly distributed throughout the year and often completely lacking of rainfall during dry period . The present study is to evaluate the physico-chemical characteristics of groundwater in and around a Noyyal River basin and integrated with landuse/landcover data for the assessment and suitability of groundwater for drinking purposes.
Based on its function, watershed is divided into three parts, namely upstream watershed, central watershed, and downstream watershed. Upstream watersheds are based on managed conservation functions to maintain the watershed environment conditions that are not degraded, which can be indicated by watershed vegetation cover conditions, waterquality, water retention, and rainfall. The central watershed is based on a riverwater utilization function that is managed to provide benefits for social and economic interests, which can be indicated from water quantity, waterquality, water delivery capacity, and groundwater levels, and is related to irrigation infrastructure such as river management, reservoirs, and lakes. Upstream watersheds are based on riverwateruse functions that are managed to provide social and economic benefits, which are indicated through the quantity and quality of water, water delivery capabilities, rainfall levels, and related to agriculture, clean water and wastewater .
For the remainder of livestock within catchments we were able to sum excreted P from cattle, sheep and IPPC licensed pigs into a single variable to estimate a combined effect. In the linear model the parameter estimate on the livestock P variable (LnLivestockP) suggests an elasticity of 0.17; proportionately more livestock within a catchment leads to a proportionate deterioration in lake waterquality. But livestock P was one of the variables that we found had a non-linear effect on lake waterquality. While the parameter estimates associated with LnLivestockP and LnLivestockP 2 in the non-linear model are individually not statistically significant, when we calculate the marginal effect of livestock P on waterquality the estimated effect is statistically significant. The marginal effect of livestock P on lake waterquality in the non-linear model is given by the term 0.007 + 0.024*LnLivestockP. We used the delta method (Greene, 2000) to construct standard errors, which are presented in Table 4. Where the amount of livestock P within a catchment is relatively small, the estimated marginal effect is statistically insignificant. But at higher levels of livestock P the marginal effect is increasing and statistically significant. The marginal effects calculated in Table 4 cover the range of livestock P for the catchments within the dataset. For catchments with roughly 1800 tonnes of livestock P the estimated elasticity is 0.18, which is practically equivalent to the single elasticity estimate in the linear model. Within the dataset there are two catchments with annual excreted livestock P of roughly 1.4 million tonnes and one in excess of 5 million tonnes. The elasticity estimate in these catchments is 0.34 and 0.37 respectively, both significant at the 90% level.
sedimentation, denitrification, reduced base flow, and altered channel morphology and
ecological structure and function (Atasoy et al., 2006; Walsh et al., 2005). Urban streams tend to be flashier, meaning more frequent and larger flow events. As a result of increased
impervious surfaces and piping, the volume and transport rate of runoff increases (Walsh & Roy et al., 2005). Even low intensity catchment urbanization can increase nutrient and pollutant concentrations of urban streams (Walsh et al., 2005). As land-use changes from rural to urban, construction and erosion increase the amount of sediment entering watershed streams. This clogs streams, increases turbidity, decreases light penetration and photosynthesis (Atasoy et al., 2006). Excess sediment damages respiratory organs in fish and interferes with spawning (Atasoy et al., 2006). Thus, total suspended solids (TSS) can be an effective indicator of urbanization and its potential adverse effects. Urban streams have higher sediment
Most seasonal variations in riverwater chemistry are driven by climatic and biotic factors and are therefore largely governed by the processes that are taking place in the terrestrial part of the watershed such as natural or human induced vegetation cover changes (Moldan and Cerny, 1994). Our results show slight seasonal diﬀerence in the interaction between the landscape factors and waterquality. Waterquality was better explained by interactions with the landscape in spring and fall rather than in summer. This may have been the result of relatively higher discharge within watersheds of this region between fall and spring, as well as tighter nutrient spiraling (Elwood et al., 1983), increased interception capacity and reduced overland runoﬀ associated with the growing season (Dunne and Leopold, 1978). This is further supported by the general lower concentra- tions of most nutrients and pollutants within the sub- watersheds in the summer. The signiﬁcantly higher concentrations of nutrients in the late winter and spring also coincide with the period of fertilization of agricultural ﬁelds in the area. Nutrients are easily transported to the channels via surface runoﬀ and subsurface ﬂows during this period (Osborne and Wiley, 1988).
Wetlands are important biodiversity hotspots in Zimbabwe that provide useful resources to sustain humans and wildlife. They are a source of water for drinking and agriculture, especially during the drier seasons. Wetland vegetation is an integral feature of wetland ecosystem, which constitutes the primary producers for the food chain. It is also a vital habitat for other species like phytoplankton, zooplankton vertebrates, and invertebrates. However, previous studies (Ndhlovu, 2009; Murungweni, 2013; Marambanyika and Beckedahl, 2016) have shown that wetland ecosystems are being degraded by human activities such as agriculture and urbanisation and Mzingwane catchment wetlands are no exception. The situation is further compounded by current climate variability and change, which has seen the catchment experiencing recurrent droughts and increased temperatures (Sibanda et al., 2017). Such climatic anomalies will no doubt affect the availability of water and modify wetland ecosystems. Literature shows that more than half of the world’s wetlands have been disturbed, transformed and degraded in the last 150 years (Gardner et al., 2015). In response to this problem, the study sought to ascertain climate variability and change in Mzingwane catchment through the use of historic and current climatic trends in rainfall and temperature and modelling impacts of LU/LC changes on nested wetlands as well as through simulating future rainfall and extreme events and their implications on wetland dynamics. It is hoped that the results will help in the crafting of relevant and sustainable policies and strategies for the management of local level wetlands for the benefit of both humans and ecosystems.
There are two distinct rainy seasons in the catchment: March-April- May (the long rains) and October-November (short rains). The mean annual rainfall ranges from 1300 mm to 450 mm and daily temperatures ranges from 10 o C in the upper zone of the region to over 30 o C in southern zone. The soils in the study area display spatial variability. The upper zone consists of andosols, nitisols, cambisols and portions of phaeozems. The middle zone consists of nitisols and cambisols, while the southern zone is dominated by cambisol with minor portions of gleysols and ferrasols. Landuse pattern within the Upper Athi catchment are highly influenced by rainfall patterns, topography and human activity. Agriculture dominates the economy of the highlands in the North West and western parts. This changes significantly moving away towards the middle and the southern parts. Industrial activities dominate middle zone, while livestock and small- scale irrigation are pronounced in the southern reaches of the
Abstract Patches of landuse/cover types determine the amount and rate of rainfall runoff produced. This study investigated the susceptibility of disturbed and undisturbed soils to rainfall-runoff generation and volume from diverse landuse/cover types. Comparative in-situ soil infiltration experiments were performed, while the curve number based model was adopted to estimate runoff volume. The results show that the diversified landuse/cover types had a significant effect on the content of nitrogen, sand, phosphorus and organic matter (P<0.05) than on the percentage of clay and silt parameters in the cultivatable soil layer. The anthropogenically disturbed forest patches and agricultural landuse (cotton and beans) types had relatively higher rates of soil infiltration (>40 mm/hr.) compared to the rates in rice and woodland. In principle, the subsistence agricultural landuse is statistically qualified as the primary contributor of rainfall-runoff generation followed by disturbed forest patches and scattered bushlands in the catchment. This was demonstrated by the moderately lower rates of saturated hydraulic conductivity in the agricultural landuse types which later translated into an increase in the catchment streamflow. In addition, the curve-number model also posted higher rainfall runoff volume (71,740 m 3 ) above average generated from agricultural landuse types followed by bushlands (42,872 m 3 ) from any given single rainfall storm in the studied tropical rural catchment.