MODELLING NON-POINT SOURCE SOURCE … · environmental and water resources managers in ......

* Obinna C. D. Anejionu** Francis I. Okeke

Abstract

Nonpoint source pollution (NPS) is the term used to describe pollutions generated from diffused sources. It comes from runoff, such as rainfall or snowmelt moving over and through the ground, and picking up pollutants such as, nutrients from sediments, manure, pet wastes, fertilizers, and automotive grease, as they move. Nonpoint source pollution has been identified as a major contributor to the deterioration of water quality, as a large proportion of pollutants (sediments from agricultural land, fertiliser, industrial wastes, etc) entering water bodies usually come from nonpoint sources. The paper is aimed at using the Universal Soil Loss Equation (USLE), GIS, and remote sensing techniques to model nonpoint source pollution of the southern portion of Enugu State, to estimate the annual sediment loss from the area and identify erosion high risk areas. The results are expected to help environmental and water resources managers in the state in formulating policies, plans and strategies for controlling the deterioration of water resources and the environment in general in the study area, as well as agricultural managers involved with improving soil fertility and agricultural production. Remote Sensing image classification technique was used on Landsat ETM+ image covering the area of study, to create a land use/land cover map of the study area that was used to estimate some of the USLE parameters (C), while GIS techniques were used to carry out various manipulations on the other USLE parameters and to create the model. The results estimated the total amount of sediments lost annually lost from the study area to be about 656.575tons/year and also identified certain critical areas of interest (erosion hotspots) that contribute significant amount of sediment in the study area.

Keywords: Nonpoint source pollution modelling; GIS and erosion modelling; GIS in Sub-Saharan Africa; Nonpoint source pollution modelling; Erosion modelling in Nigeria; Enugu Nonpoint pollution modelling

MODELLING NON-POINT SOURCE POLLUTION OF THE SOUTHERN SECTION OF ENUGU STATE

THROUGH GIS AND REMOTE SENSING

* Department of Geoinformatics and Surveying, University of Nigeria, Enugu Campus, Enugu State Nigeria **Department of Geoinformatics and Surveying, University of Nigeria, Enugu Campus, Enugu State Nigeria

Corresponding Author

Background Nonpoint source pollution (NPS) is defined as the “pollution originating from urban runoff, construction, hydrologic modification, silviculture, mining, agriculture, irrigation return flows, solid waste disposal, atmospheric deposition, stream bank erosion, and individual sewage disposal” (US Environmental Protection Agency, 2002; MassDEP website, 2008). It is a major cause of water quality problems in the world, as pollutants on land are released and transported during rainstorms into water bodies (Mertz, 1993, Okeke, 2006). The degree of deterioration depends on the strength of pollution sources and the delivery process of the pollutants from the source to the receiving waters (Novotny and Chester, 1989).

Unlike point sources of pollution such as a factory, nonpoint source pollution occurs from multiple sources. Nonpoint source pollution has drawn the attention of the

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general public, due to its contribution to the pollution of surface and subsurface drinking water sources, ubiquitous nature, and potential chronic health effects (Corwin and Wagenet, 1996). However, it is usually difficult to estimate due to the diffuse nature of it sources that makes them often hard to identify.Modelling non-point source pollution through GIS provides a cost effective approach for estimating the degree of deterioration caused by these pollution sources. It is an important step in environmental management, especially in controlling the degradation of water resources. It quantifies the quantity of sediments that is deposited into water bodies in any watershed, as well as highlighting areas of high erosion risks. Numerous nonpoint pollution models (empirical and theoretical) ranging from simple to complex, with many of the models, lacking in temporal and spatial dimensions (Levine, et al, 1993) have been created to address nonpoint source pollution issues. The GIS based models have generally shown greater capability for assessing nonpoint solution models.Yoon, (1999), developed methods for directly linking the distributed parameter model, Agricultural Nonpoint Source model (AGNPS) with a Geographic Information System (GIS) and a relational database management system (RDBMS) to investigate a nonpoint source pollution problem. The AGNPS, an event-based model, simulates runoff and the transport of sediments and pollutants from mainly agricultural watersheds. Kang and Bartholic (1994) integrated distributed simulation models, databases, GlS, and Expert Systems (ESs), to demonstrate a state of the art solution for the standard three-step nonpoint source pollution management procedure. James and Hewitt (1992) illustrated the use of GIS with the Water Resources Evaluation of Nonpoint Sivicultural Sources (WRENSS) model, used to assess nonpoint pollution in the Blackfoot River drainage basin in Montana, USA. It has also been found that at a regional level, GIS allows the most feasible sites for controlling nonpoint pollution to be identified so that planners can concentrate on such sites and take up more complicated sites at a later stage (Atkinson, 1988).However, research into nonpoint source pollution in Nigeria is scarce. This is one of the motivations for this paper.

The Study Area

Enugu State is a mainland state in the southeast region of Nigeria, carved out of the old Anambra State in 1991 with its capital at Enugu, from which the state derives its name (Enu Ugwu Top of the Hill). The state made up of 17 local government areas (See Figure 1) and four principal cities (Enugu, Udi, Oji and Nsukka) is bordered to the east by Ebonyi State, to the northeast by Benue State, to the northwest by Kogi State, to the west by Anambra State, and to the south by Abia and Imo States. It has a population of about 3,267,837 according to the 2006 national census (National

2Population Commission, 2010) and an area of about 7161Km (716100 Hectares). It rose to geopolitical significance in the early twentieth century, due to the discovery of coal in commercial quantity in 1909 by a team of British geologists led by Albert Kiston. This brought about the emergence of a permanent cosmopolitan settlement making it the oldest urban area in the Igbo speaking southern part of the country (Enugu State Government, 2010; Idu, 2009).

The state with its good soil and climatic conditions that support agricultural activities, is predominantly rural and agrarian (a substantial proportion of its population engages in farming), and the rest of the working population engage in trading, services, and manufacturing activities (located mostly in Enugu, Oji, Ohebedim, and Nsukka).

Enugu State located at an altitude of about 223m above sea level, with an undulating topography, dotted with knolls and hills, is located between latitudes 7° 6' 36''N and 5° 55' 15''N, and longitudes 6° 55' 39'E and 7° 54' 26'E. The state is located at the tropical rain forest belt with a derived Savannah (Sanni et al., 2007; Reifsnyder and Darnhofer, 1989) and lying within the Cross River basin and Benue trough. The Precambrian basement rock in the region is overlaid with sediments bearing coal from the Cretaceous and Tertiary age, and the highlands surrounding it underlain by sandstone for the most part, and the lowlands by shale, with much of the escarpment stretching around it ravaged by soil and gully erosion (Egboka, 1985). It has mostly tropical savannah climate and is very humid, with humidity peaking between March and November (Reifsnyder and Darnhofer, 1989). The climate is marked by the rainy and dry seasons, as in the rest of West Africa, and the mean temperature ranges between 30.64 °C and 15.86 °C.

The Tropical Environment Modelling Nonpoint Source Pollution of the Southern Section of Enugu State through GIS and Remote sensing

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Figure 1. Map of Enugu State showing the locations of the 17 local government areas Sources: (Oji River Peoples Forum, 2011)

Some of the prominent rivers include the following: Ekulu, Asata, Ogbete, Aria, Idaw, Nyaba (Ofomata and Umeuduji, 1994), Ajali, Mmiriocha, a tributary of Ajalli River (Ononugbo et al., 2010), Oji, Adada, Nnom, Du, Mamu, Ozom rivers, as well as some prominent lakes including the Nike, (Gwurugwuru) Ezeagu (Iheneke), Opi, Amagunze/Akpawfu, Ani Ozalla lakes. MethodologyThe methodology comprised the simulation of nonpoint source pollution through the use of USLE in a GIS environment. ArcGIS Spatial Analyst tools and the model builder were used to build the individual factors of the equation and to create the model.

Data

Soil map showing the locations of 5 soil types within the study area was obtained from the results of an earlier research (Okeke and Nkwunowo, 2007), digital elevation model (DEM) and monthly mean precipitation dataset obtained from WorldClim climate data repository, and Landsat ETM+ image covering the study area, obtained from Global Land Cover Facility, were the primary data used in the project.

Data Processing

The Landsat ETM+ image was classified, using ER Mapper image classification tools, to obtain a land use map of the study area. Training datasets for the classification were obtained based on image interpretation of a Quickbird image of Enugu town and environs. The land use/land cover map comprises 7 major land use classes Figure 2. The soil map originally in vector format (Nkwunowo and Okeke, 2007) was rasterised, in order to get it into usable forms for subsequent calculations, Figure 3.

Figure 2. Enugu Land use map obtained from Landsat ETM+


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The Model

The model developed in this project combines empirical models with spatial parameters affecting the movement of pollutants through the landscape. Considering a watershed with a permanent stream, when a storm occurs, part of the water from the storm infiltrates through the soil and flows as subsurface flow, while the other part is carried down the slope as overland flow. The intensity and duration of the storm, the moisture content in the soil prior to the storm, and the permeability of the soil are the core factors that determine the amount of water that is carried as overland flow. And as the soil becomes saturated with moisture, the amount of water carried as overland flow increases. Furthermore, soluble sediments and nutrients in the soil detach and dissolve in the water, and are carried away by the water into the stream network. The amount of sediments and nutrients available for transport by water depends on land use, soil, and topographic conditions, which is calculated through the simulation of the Universal Soil Loss Equation (USLE).

However, not all of the available sediments and nutrients in the soil are carried away in the process outlined above. Surface conditions such as soil permeability, slope, and vegetation density determine the proportion of sediments and nutrients that may be physically “trapped” in situ and the proportion that could be carried away by water. The proportion eventually carried away by water is known as the delivery ratio. This ratio is calculated for each cell in the study area as “overland flow delivery ratio” in the second stage of the model (Levine et al 1993).

Furthermore, during an intense storm, a network of temporary streams usually forms in a watershed. The model assumed that the energy of overland flow within these temporary streams is high enough to mobilise and carry away all available sediments and nutrients within it. This means that the delivery ratio for cells within these temporary steams is 1, that is to say that 100% of the available nutrients and sediments will be carried away by water. The network of permanent and temporary streams in a watershed is delineated in the third step of the modelling as “stream network delineation” (Levine et al., 1993).

The fourth step involves determination of the path of flow of pollutants from each cell to the watershed outlet. The individual path of water flow from each cell towards the stream network is identified. The length of this path determines the contribution of each cell to the total pollutant load (the further a cell is from the stream the smaller its contribution since a portion of the initial sediments and nutrients carried out of the

cell will be trapped in each consecutive cell on the way to the stream). This is used in conjunction with the delivery ratio to determine the part of available sediment load that eventually reaches the stream termed “total flow path delivery ratio” (Levine, et al., 1993). The result obtained above was subsequently used to compute the total mass of sediments delivered from each cell in the area to the streams in the final step of the model.

Figure 3. Rasterised version of Enugu soil map, showing the distribution of 5 soil types


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Sediment Detachment CalculationThis stage involves calculating the initial mass of sediments available for transport by water flow. The Universal Soil Loss Equation (USLE), a widely used empirical model, was used to compute the initial mass of sediments available for transport.The USLE is defined by the following expression:

A = R * K * L * S * C * P

Where:A = average annual soil loss per unit area (tons per cell per year); R = the rainfall runoff erosivity factor (MJ·mm/ha/yr); K = soil erodibility factor; L = slope length factor; S = the slope steepness factor; C = the cover and management factor; P = the support practice factor (Wischmeier and Smith, 1978).

Rainfall Runoff Factor (R) (Eroxivity Index)The runoff factor is an index of how much erosive force a typical storm has on surface soils. The factor was computed using the following equation:R = 38.5 + 0.35 × Pr

Where, Pr = is the annual average rainfall (mm/yr) (Lee and Lee, 2006).The annual average rainfall was computed from the monthly mean precipitation datasets using ArcGIS map algebra as shown below:Average Annual precipitation = (Prec + Prec + Prec + Prec + Prec + Prec + _1 _2 _3 _4 _5 _6

Prec + Prec + Prec + Prec + Prec + Prec )/12_7 _8 _9 _10 _11 _12

Where, the numbers describe the particular month of the year (Prec = Mean _1

monthly precipitation for January).

Soil Erodibility Factor (K)The soil erodibility factor is an empirically derived index showing how susceptible a soil is to rainfall and runoff detachment and transport, based on soil texture, grain size, permeability and organic matter content. The higher the value of K, the more susceptible the soil is to soil erosion. The values of K for the different soil types within the study area, Table 1was used to reclassify the soil map dataset, to obtain the soil erodibility raster dataset of the study area

Table 1: Soil Erodibilty values

Soil Type K factor

Loamy Fine Sand 0.11

Concretionary Clay 0.17

Sandy Clay Loam 0.20

Sandy Loam 0.13

Silty Clay 0.26

Source: Stone and Hilborn, 2000

Slope length factor (L)The slope length factor was calculated using the following formula:

xL = (l/22)Where:l = length of flow across a cell (in metres); x = slope factor defined as:0.5 for slopes > 4% (or 0.04)0.4 for slopes = 4%0.3 for slopes < 4%

To compute the slope length factor, the slope factor and length of flow were first determined. The slope was computed from the DEM in degrees and converted to percentage slope using the following expression:

S = tan(S )% deg

The slope obtained was subsequently reclassified with the above values to obtain the slope factor dataset. The length of flow represents the distance that water and pollutants are transported through a cell. This was determined using the Flow Length tool under ArcGIS Spatial Analyst's Hydrology tool. As the length of flow is dependent on the flow direction, this was initially computed using Spatial Analyst tool as well.

Slope steepness factor (S)The slope steepness factor was computed using the following equation developed by Nearing (1997).

171.5

1 exp(2.3 6.1sin)S

????

? ?


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Where, = Slope (in degrees)

Cover and Management Factor (C)The cover and management factor, an index that indicates how crop management and land cover affect soil erodibility, C values for the land use types within the study area were obtained from guidebooks (Lee and Lee, 2006; Shi et al., 2002) as shown in Table 2 below. The values were subsequently used to reclassify the land use map data.

Table 2: Cover and management factor values

Source: Lee and Lee, 2006; Shi et al., 2002

Support practice factor (P)The P- factor refers to the level of erosion control practices such as contour planting, terracing and strip cropping, put in place in the watershed. It depends on the average slope steepness within the study area. The following values shown in Table 3 were used to reclassify the soil map dataset to obtain the P factor for the study area. Since the predominant practice in the study area is contouring, and the slopes fall within 0 -10.091%, the values 0.55 and 0.60 were used.

Table 3: P factor depending on cultivation types and slope

è

Code Land Use C 1 Water 0.000 2 Barren 0.500 3 Developed 0.003 4 Light

Vegetation 0.05

5 Agriculture 0.3 6 Forest 0.004 7 Swamp/Muddy 0.002

Slope Contouring Stripping Terracing

0.0 -0.7 0.55 0.27 0.10

7.00 –

11.3

0.60

0.30

0.12

11.3 –

17.6

0.80

0.40

0.16

17.6 –

26.8

0.90

0.45

0.18

26.8 > 1.00 0.50 0.20

Source: Korea Institute of Construction Technology, 1992, cited in Lee and Lee, 2006.

Computation of AHaving computed the necessary factors for the determination of the average annual soil loss per unit area (tons per cell per year), the value was computed in the model through multiplication of all the factors.

Computation of the overland flow delivery ratioThe influence of surface conditions, such as soil permeability, slope and vegetation density on the delivery of sediments and nutrients during movement toward a stream channel in the area was calculated at this stage. The trapping efficiency, which shows the proportion of sediments that could be physically trapped in a cell, was computed from the following equation:

(-3.57 0.33x + 0.01sqrx + 22.82 + 0.73p)TSS = 1 / (1 + e )trapped

Where:TSS = trapping efficiency for total suspended sediment; x = length of flow (metres); sqrx = length of flow squared (metres squared); p = soil permeability (inches/hour);

x= slope angle (radians); e is the base of natural logarithms, i.e. ~2.718281828.

The soil permeability values shown in Table 4 were converted to equivalent values in inches/hr, and used to obtain the soil permeability factor dataset through the reclassification of soil map dataset. The slope dataset in degrees already computed, was further converted to radians values, with raster calculator tools, using the following expression:

S = S * 22 /1260rad deg

These values were used in conjunction with the flow length already computed to obtain the trapping efficiency.

Table 4: Average permeability for different soil textures

è

Soil Type

Sand

Sandy loam

Sources:ftp://ftp.fao.org/fi/CDrom/FAO_Training/FAO_Training/General/x6706e/x6706e09.htmand http://dese.mo.gov/divcareered/AG/CDE/SoilsInterpretation.pdf


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Subsequently, the delivery ratios, which show the proportion of sediment that could escape from a cell during runoff was obtained through the subtraction of the trapping efficiency from 1.

Delineation of the Stream Network Sediments entering a stream cell are assumed to be carried by water energy downstream to the next cell, thus the delivery ratio in cells falling within a drainage stream is usually assigned the value of 1. However, the delivery ratio calculated above did not distinguish between cells that are outside the stream and cells that fall into the stream. Therefore, the streams in the area have to be delineated, and cells falling within the streams, assigned the delivery ratio of 1.To obtain the stream network, the flow accumulation was first derived and the result obtained, reclassified by assigning a value of 1 to cells having values greater than or equal to 5000, as a cell having 5000 or more cells flowing into it is considered part of the drainage stream, while those with values less than 5000 were assigned a no data value. Levine et al (1993) used a threshold of 15 for their study area due to the watershed characteristics and cell resolution of their data. The reclassified dataset was overlaid with the delivery ratio image, using the Map Algebra merge function, to obtain the cell delivery ratio, which represents the proportion of pollutant load in a cell that could be transported to the next cell in the flow path.

Total flow path delivery ratioThe total flow path delivery ratio, which is a representation of the proportion of the pollutant load in a cell that actually reaches the water outlet in the area, was calculated by linking the cell-based delivery ratio with the flow path data, using the following expression: Total Flow Path Delivery Ratio = [Cell Delivery Ratio] * [Flow Accumulation] [Maximum Flow Accumulation]

Total annual sediment loadings per cellThe actual mass of pollutants delivered to the water outlet from each cell was calculated by multiplying the potential sediment loadings for each cell in the area (A) with the total flow path delivery ratio for the sediments (see Figure 4). The total amount of soil lost calculated through the summation of the values of all the cells in the area.Results The model estimated that about 341,746 tons of sediment would annually be available for transport in the study area (potential sediment loading). However, out of the available sediment from each cell ready to be transported, the model showed that

only about 657 tons actually got delivered to the water outlets in the area in a year, See Table 5. This huge difference in the potential sediment loading and the amount of sediment lost is as a result of the sediment delivery process in the area (total flow path delivery ratio), which is dependent on the stream network, and surface conditions (land use, soil permeability, and slope). The result also identified critical areas within the study area that significantly contribute to the total amount of sediment that is eroded from the area. These areas highlight parts of the study area that require urgent remediation. The erosion risk map, Figure 4 produced from this has five classes relatively grouped according to level of risk identified as shown in Table 6. The delineation of such areas is expected to play a vital role for decision makers involved in the management of erosion in the area as well as facilitating remediation processes.

Table 5: Summary statistics.

Parameter

Table 6: Erosion risk level.

Risk Level

Figure 4. Erosion risk map of a part of Enugu (Author)Sources:


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DiscussionThe results raise strong concerns for environmental managers involved with the mitigation of environmental degradation in the study area, due to the amount of soil annually lost from the area, as identified by the model. With increasing percentage of impervious surfaces in the area through industrialisation and urbanisation (Okeke, 2006), the amount of sediment deposited into the water outlets and other parts of the environment is expected to rise. Soil fertility is also adversely affected by the amount of sediments that is carried away annually from the area. The identified erosion hotspots (areas) should be targeted for immediate remediation actions.

ConclusionTo improve water quality and mitigate the impact of environmental pollution, environmental managers make decisions based on data they are able to gather. However, data collection can be an expensive, tedious and time consuming exercise and as most environmental monitoring agencies have limited funds to carry out such exercises, modelling approaches tends to be the obvious choice. GIS modelling of nonpoint source pollutions provides an easy and cost effective way of simulating the interplay between various components of the environment that contribute to nonpoint source pollution. The results obtained from this research estimated the amount of sediments eroded from the study area as well as highlighted parts of the area that require urgent intervention by environmental managers in order to check the menace of erosion in the affected areas. The model showed that about 657 tons of sediments are annually deposited into the water outlets in the study area. This result is expected to ginger environmental management experts in the state into action, and would help in the formulation of policies and implementation of measures that would be used to effectively combat the ongoing degradation of water resources and aquatic life in the area.This research has once again highlighted the important role GIS and Remote Sensing play in environmental management. The flexibility and robustness of GIS in handling large volumes of varied geospatial information from disparate sources and analysing environmental conditions covering large areal extents, such as nonpoint source pollution in a cost effective and efficient way cannot be overemphasised.

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