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-O-O[Z-O-O-O-O-O��[Z-O-O-O��-O-O-O-O���������������������������������������������������������������������-O-O-O-O-O-O-O-O-O�7	�C:	Effects of Land use change on Hydrologic Models:
A case study of urban basin Accra
P. A. Owusu1,* E. K. Nyantakyi1 and J. K. Borkloe1
1Department of Civil Engineering, Kumasi Polytechnic, Kumasi, Ghana.
Corresponding author: Kumasi Polytechnic, Department of Civil Engineering, P. O. Box 854, Kumasi, Ashanti, Ghana.  HYPERLINK "Tel:00233244543323" Tel:00233244543323	 Email: princeappiahus@gmail.com
Abstract:
The study assesses the effects of land use and land cover changes on the hydrology of Odaw basin in Accra, Ghana. Digitized and LandSat TM image of 1964 and 2007 were analyzed using Erdas Imagine and ArcGIS tools. The Soil Conservation Service Curve number (SCS-CN) model was used to simulate runoff for the basin. A total of six broad land use and land cover classes were identified and mapped for both years. These were settlement, closed forest, thickets, grasslands, open forest and water bodies. The analysis showed that between 1964 and 2007, settlement and open forest increased by about 306% and 118% respectively. The study identified population growth, as a major cause of increasing curve number (CN) levels in the catchment area leading to the continuous reduction of the storage capacity of the basin by about 55% consequently creating higher overland water flows a trigger of flush floods. The coefficient of initial abstraction of the model as applied to the basin was found out as 0.09 with Nash-Sutcliffe efficiency (E), coefficient of determination (R2), and index of agreement (D) as 94.5%, 0.73 and 0.83.The method in this research are replicable in other basins in Ghana and the sub-region.

KEY WORDS: ArcGIS; Erdas Imagine; land use change; rainfall run-off model; SCS-CN runoff model; urban area; 

Introduction	
Land use changes in a watershed can impact water supply by altering hydrological processes such as infiltration, groundwater recharge, base flow and runoff. For instance, covering large watershed areas with impervious surfaces frequently results in increased surface runoff and reduced local surface erosion rates. Moreover, watershed development changes land use patterns and reduces base flow by changing groundwater flow pathways to surface water bodies. An integrated landscape model can potentially extrapolate from management practices and land use pattern to determine potential environmental impacts [1]. Thus, the development of an integrated approach that can simulate and assess land use changes, land use patterns and their effects on hydrological processes at the watershed level is crucial to land use and water resource planning and management. Numerous studies have developed modeling approaches to simulate the pattern and consequences of land use changes. Different types of models are used to explore land use changes, such as stochastic models, optimization models, dynamic process based simulation models and empirical models. Recent studies include those conducted by [2 -11].
Empirical analysis is applied to determine the relationships between land use spatial distribution and a number of factors that are the drivers and constraints of land use. Based on the competitive advantage of each land use at a location the competition among land uses for a particular location is simulated [12].
Often, the assessment of land use change results in changes in landscape pattern. Landscape composition, configuration, and connectivity are primary descriptors of the landscape patterns [1]. Landscape patterns can be quantified using spatial landscape indices or metrics to characterize and quantify landscape composition and configuration. The composition of a landscape denotes the features associated with the variety and abundance of patch types within a landscape. The spatial configuration of a landscape denotes the spatial character and arrangement, position, or orientation of patches within class or landscape [13]. These metrics may include the number of patches, area, patch shape, total edge of patches, nearest neighbor distance, landscape diversity, interspersion and contagion metrics to represent landscape patterns, including compositions and configurations. Recent studies have applied landscape metrics to quantify landscape patterns [14 -17]. 
Hydrological models provide a framework to conceptualize and investigate the relationships between climate, human activities (e.g., land use change) and water resources [19]. Distributed hydrological models on a watershed scale are frequently used for quantifying the impact of land use change on hydrologic components [20]. The generalized watershed loading functions model developed by [21] is a combined distributed/lumped parameter watershed model that can simulate runoff, sediment, and nutrient loadings in watersheds given source areas of variable sizes(e.g., agricultural, forested, and developed land). Surface loading is distributed in the sense that it allows multiple land use and land cover scenarios in which each area is assumed to have homogeneous attributes when addressed by the model [22]. In this study, an integrated approach that combines land use, landscape metrics and hydrological models is used. Land use scenarios that differ with respect to planning policies and land use requirements are analyzed for their effects on landscape pattern, surface runoff, groundwater discharge and stream flow of the watershed. 
The Soil Conservation Service (SCS, 1972) model estimates precipitation excess as a function of cumulative precipitation, soil cover, land use, and antecedent moisture. The keystone of the SCS equation is the soil curve number (CN), function of soil classification and land use or cover.
[23] assessed the impacts of land use change scenarios on hydrology and land use patterns in the Wu-Tu watershed in Northern Taiwan using the SCS-CN model. Analysis results revealed that future land use patterns differed between spatial policies. Scenarios with low land use demand for land use conversion policies did not lead to significantly different land use patterns. Moreover, patterns of future agricultural land patches obviously differed among agricultural land conversion policies. The stream flow, runoff and groundwater discharge were successfully simulated using a lumped hydrological model that can assess the impact of land use change in the watershed. [24] worked on the hydrological modeling of ungauged Wadis in arid environments a case study of Wadis Madoneh in Jordan using the SCS-CN model. The model was used to simulate streamflow of the Wadis Madoneh catchment. It was realized that the combination of these techniques with the SCS model makes the runoff estimate more reliable.
There are several advantages of the SCS-CN over other models. It is a simple conceptual method for estimation of direct runoff amount. However there are limitations in that it does not contain any expression for time and ignores the impact of rainfall intensity and its temporal distribution. Again there is a lack of clear guidance on how to vary the antecedent moisture condition, especially for lower curve numbers and or rainfall amounts and its application to large catchments.
The Odaw catchment is part of the coastal basins in Accra, Ghana. There are recent reports of serious flood cases within the basin, a situation attributed to rapid urbanization.
This is explained in the fact that as urban areas increase, vegetated land is converted to impervious surfaces, consequently increasing runoff velocity and runoff volume leading to peak runoff also increasing and the time to peak decreasing [25]. 
Thus, the increase in the urban areas, contributes to increase in flooding because it reduces the natural storage capacity of the soil, removal of floodplain storage for settlement and consequently contributes to increased runoff.
Modern technologies such as remote sensing and Geographic information systems (GIS) provide some of the most accurate means of measuring the extent and pattern of changes in landscape conditions over time [26, 27]. 
The main aim of this study was to assess the impact of land use and land cover changes on the hydrology of the Odaw catchment. The specific objectives were; quantify the changes in land use and land cover, to accurately quantify the ratio of initial abstraction to maximum water potential (Ia/S) value in order to increase the precision of estimation and to establish its effect on rainfall runoff processes. This river basin is located in one of the fast developing regions of Ghana and unfortunately limited studies are available to provide information of the dangers of surface imperviousness. This information is particularly important to water resource managers, town and country planners and environmental and sanitation policy developers. 

Study Area 
The Odaw River is the major stream draining central Accra, Ghana with its outlet into the Korle Lagoon, while smaller streams lead into lagoons to the east and west of central Accra. Much of the Odaw catchment area is built up and many of the streams are channelized. Rainfall in Accra occurs in the form of intensive storm events, which cause local flooding. The total population of Enumeration Areas that border the Odaw and its tributary streams is roughly 172,000 people based on the 2000 census (Ghana Statistical Services, 2000).
	The Odaw basin lies in the coastal area of Accra, Ghana. The geographical coordinate of the basin are latitude [0.00, 5.88N] and longitude [0.15W, 0.00]. The Odaw basin is approximately 39.7km long and covers an area of 365.3km2. The widest part is just about 12.5km. The study area lies in the Coastal Savannah zone within tropical climate [28]. Rainfall depends on the north-south migration of Inter Tropical Convergence Zone. 
Average annual rainfall is about 730mm. The mean annual temperature is 26.8�C. Elevation varies between 8m and 414m above sea level. The soil type of the Odaw basin originates from the Dahomeyan system (metamorphic) basement rocks of middle-late Precambrian age and consists of quartz schist. The land use is of varying functions. �Fig. 1� shows the Odaw basin.
Hydrological soil groups of the basin are determined on the basis of information from statistics of the processed shapefiles of soil map in ArcGIS, �Fig. 2�. It is evident from the figure that the region comprises of Hydrologic Soil Group Acrisols (HSGA), Hydrologic Soil Group Leptosols (HSGC) and Hydrologic Soil Group Luvisols (HSGD) respectively. The basin is predominantly sandy loamy and sandy clay loamy type soils with relatively low drainage properties. �Table 1� gives the hydrological distribution of the basin. 

	
Materials and Methods
In this study, a variety of data concerned with runoff estimation such as satellite images, digitized image, metrological data, soil type data and stream flow data were obtained. Erdas Imagine together with ESRI ArcGIS were used in the processing and analysis of the images. SCS-CN rainfall runoff model was used to generate the runoff and subsequently calibration and validation of the run off model. The summary of the methodology and the phases used in the study is described below. 

Data Collection
The data for the study was acquired from various state agencies. It includes soil characteristics of the basin �Fig 2�, meteorological data covering the rainfall time series �Fig 3� and fifty three months of stream flow data �Table 2�. Digitized land use map of 1964 at a scale of 1:250,000 and satellite image for 2007 at a resolution of 30m were downloaded from the European Digital Archive of Soil Maps (EuDASM) and Global Land Cover Facility (GCLF) at the University of Maryland, USA websites respectively. The images were used for land cover classification through the ERDAS imagine. 

Image Processing
LandSat Thematic Mapper (TM) image of scene (path: 193, row: 056) of year 2007 was used for the study. Remote sensing tools: Erdas Imagine and ArcGIS were used for the processing of the images. The raw satellite image was converted from Tag Image file format (Tiff) to Imagine (img) format using Erdas in order to be compatible with other Erdas Imagine files. The layers were stacked and sub-set to delineate the catchment area for classification. The UTM Zone 30N Coordinate on the WGS 84 was used to geocode the imported image. The EuDASM image for 1964 for Accra Plains was digitized in ArcGIS to obtain the 1964 land use data for the basin. This was followed by georeferencing using the Tranverse Mercator projection with reference units in meters to allow compatibility with other digitized images for further processing. 
Band combination of red, blue and green was used to display the raw images in standard colour composites. The spectral band combination for displaying images often varies with different applications [29]. This was necessary for the visual interpretation of the images. A band combination of red, blue and green (RGB) is often used to display images in standard colour composites for land use and vegetation mapping [29]. 


Land Cover Classification
The unsupervised classification method was used to classify the images into the various land cover categories. The unsupervised classification is a method of clustering. It is self-organizing in that the image data are first classified by being aggregated into natural spectral groupings or clusters present in the scene. It enables the specification of parameters that the computer uses to determine statistical patterns in the data [30]. The procedure begins with a specified number of cluster means, and then it processes the image data repetitively, assigning each of the pixels to one of the class means. After each iteration, the initial cluster means shift to represent the new statistical means of the clusters in the data. This continues until there is no significant change of cluster means. Then the land cover identities of these spectral groupings were determined by comparing the classified image to the ground reference data. The statistics of the various classes were generated using the Erdas Imagine tool.
Finally maps were composed, using ArcGIS tool and the maps were validated in the field to assess its accuracy. This was conducted through GPS known identified places in the study area as training sites. The procedure used was consistent with method which has been used in several studies, e.g. the [31]. 

Change Detection
The most commonly used land change detection methods includes i) image overlay ii) classification comparisons of land cover statistics iii) change vector analysis iv) principal component analysis and v) image rationing and vi) the differencing of normalized difference vegetation index (NDVI) [32]. The method used in this study was that of classification comparison of land cover statistics.
This method was adopted because the study sought to find out the quantitative effect of changes in the areas of the various land cover categories on surface runoff. Using the post-classification procedure, the area statistics for each of the land cover classes was derived from the classifications of the images for each date (1964 and 2007) separately, using functions in the Erdas Imagine and ArcGIS tools. The areas covered by each land cover type for the various periods were compared. Then the directions of the changes (positive or negative) in each land cover type 1964 and 2007 were determined.

Curve Number Computation
The curve number is a function of land use, soil type, and hydrologic condition. A combination of a hydrologic soil group and land use and treatment class (cover) is a hydrologic soil-cover complex. Curve numbers are assigned to such complexes to indicate their specific runoff potential. The greater the CN the greater the surface runoff volume. Antecedent soil moisture conditions (AMC) are considered in the runoff model. The initial abstraction (Ia) consists mainly of interception, infiltration, antecedent soil moisture and depression storage, all of which occur before surface runoff begins.
Curve number (CN) values of the catchment area were estimated using ArcGIS. This was accomplished by integrating the shapefiles of delineated catchment area, processed land use and digitized soil type maps produced into ArcGIS. The shapefiles were merged in ArcGIS using the arc toolbox, analysis, overlay and union tools and the resultant shapefile was added to the ArcMap project.
The attribute table of the resultant shapefile was processed incorporating the three major Hydrologic Soil Group (HSG) of the basin: Acrisols (A), Leptosols (C) and Luvisols (D) as shown in Figure 2 together with CN values based on the land use. Subsequently the weighted curve numbers (CN) were determined for the time series. Since only two satellite images were available for this study, land use change was assumed to be linear. Land use was thus linearly interpolated and used for the estimation of weighted CN value for the unknown periods.

Rainfall-runoff model: SCS-CN
The curve number (CN) method [33] is widely used for estimating storm runoff from rainfall. It is an infiltration loss model, which does not account for long term losses such as evaporation and evapotranspiration. Interception, surface storage, infiltration, evaporation and evapotranspiration are the hydrologic abstractions that occur during the conversion of rainfall to runoff. Among these various hydrologic abstractions, infiltration is the most important for storm and rainfall analysis. Evaporation and evapotranspiration are important for long-term and short-term seasonal or annual yield evaluations. Interception and surface storage are usually of secondary importance. Since the CN method is an infiltration loss model that does not account for evaporation and evapotranspiration, its use was shown to be restricted to modeling storm losses and surface runoff [34]. However, the method has been used in several long-term hydrologic simulation models with an appropriate soil moisture accounting procedure.
The SCS-CN model used in the study is a function of rainfall data, hydrologic soil group and land use characteristics. These variables were used as input for the (SCS-CN) model to derive the runoff for the basin.
The excess rainfall according to (SCS-CN) in �Eq. (1)� uses the CN as its indicator.
 EMBED Equation.3                                                                                                                    (1)
 EMBED Equation.3 
Ia= �S                                                                                                                                     (3)

Which is valid for  QUOTE P>�S otherwise, Q = 0, where Q is excess rainfall (mm) resulting from precipitation expressed as depth, P is rainfall (mm). S is the maximum potential abstraction the upper limit of water retention of the basin calculated by �Eq. (2)� and Ia initial abstraction is calculate by �Eq. (3)�. The retention parameter is related to the CN as shown in �Eq. (2)�. 
The value of S in the CN method is related to watershed characteristics and antecedent moisture conditions. The SCS-CN model sets the coefficient � at 0.2 a traditional value for the existing model. The runoff procedure used for this study is summarized in the flow chart in  Fig. 4 .  The above equations can be summarized as shown in  Eq. (4) .  
 EMBED Equation.3                                                                                              (4)

The model shows that runoff (Q) is a function of CN and that it increases with increasing CN.
Hence the CN for the time series were used as a major factor to see its corresponding effects on the rainfall model.
 

Model Calibration and Validation 
Calibration is the process of estimating values for model parameters that achieve simulation results that best reproduce observed data. Calibration of the watershed model requires gauged rainfall and stream flow observed data. For a given rainfall input, the model parameters are adjusted until computed stream flows are as close as possible to the actual observed stream flow. Klemes split-sample test method (1986) was applied in the process. In this method, the whole observed dataset is split into two independent datasets; the first set of data was used in calibration, whiles the other was used in validation of the calibrated dataset. Thus one set of the data was used in calibrating the model by soundly fine-tuning its physical and numerical parameters by trial-and-error to minimize difference between model results and field observations. Afterwards, the second independent data set is used to validate the model. The objective of the calibration procedure was to optimize the initial abstraction of the SCS-CN model in such a manner that the calculated efficiency (E) would be highest. The model has two basic variables, rainfall which is natural event and maximum storage potential (S), which is a derived parameter from land use and land cover integration. Hence all input parameters are kept constant except (�) in equation 4. Afterwards, the optimised equation was used for the validation.

Model Evaluation 
The model was evaluated by two indices of agreement coefficient of determination (R2) and index of agreement (D) as described by Ali et al. (2008). R2 is given by equation 5, where Xi and  EMBED Equation.3  denotes the observed and mean of the observed data values; Yi and  EMBED Equation.3  denotes the simulated and the mean of the simulated values. The value of R2 can be interpreted, after [35], as given BY �Eq. 5�.  
 EMBED Equation.3 					(5)

The index of agreement, D, measures agreement between the observed and simulated data sets and D is given by �Eq. 6�.  D varies between 0 and 1 with near 1 being perfect agreement.
 EMBED Equation.3 						(6)

The Nash-Sutcliffe efficiency [36], E, provides a method of determining how the lower dynamics of a model compares with the higher dynamics of observed values. E also shows how well a model can predict observed values. E is given in the �Eq. 7�. 
 EMBED Equation.3 		(7)
	
Results and Discussions
 Effect of land Use Change 
A total of six land cover categories were identified and classified in the study. These were settlement area, closed forest, thickets, grassland, open forest and water body as shown in �Fig. 5�.
The comparison between the two different years of land use in �Table 3� and �Fig. 5� shows significant change detection in almost all the six land use categories and their extent during the period. The most extensive land cover change over the period was found to be settlement followed by open forest representing 306% and 118% respectively. The trend of land cover changes in the study shows general conversion to built up areas. The average annual growth rate of land use change was established approximately as 0.52%. This has potential on the catchment characteristics and hydrology. Since land cover is a function of rainfall regime, soil conditions and geomorphology, the conversion of the land cover to built up areas would definitely lead to changes in these variables of the catchment disrupting the hydrological cycle by altering the balance between rainfall and evaporation and consequently, the runoff response of the area from sub-surface flow to overland storm flows.
�Table 4� shows the weighted curve numbers over the hydrological soil groups between 1964 and 2007. The change in the land use between 1964 and 2007, �Table 5�, shows  appreciable reduction of the water holding capacity of the basin (maximum potential abstraction) about 55%, which accounts for corresponding higher levels of surface runoff. 



Model Evaluation
Due to the fact that surface runoff using the uncalibrated curve number method was not predicted well, a calibration was carried out. The basin has 53 months of observed steam flow data spanning between 1988 and 1992. From the total number of events, a range of representative rainfall-runoff events in terms of discharge amount and seasonal occurrence were taken for calibration and validation (23 for calibration and 30 for validation events). The objective of the calibration procedure was to optimize (�) in the equation Ia = �S (Eq. (3) in such a manner that the calculated efficiency (E) for all calibration event would be highest. During the calibration procedure, all input parameters were kept constant except (�), which was varied between 0.01 and 0.3. Afterwards, the optimized equation was used for validation. The highest E was found when � is 0.09.
The validation and calibration results are shown in  Fig. 6  and  Fig. 7 . The runoff model after calibration and validation was evaluated using the Nash Sutchliffe efficiency (E), coefficient of determination (R2), and index of agreement (D). The model efficiency was found to be 94.5%, with the coefficient of determination R2 being 0.73, and the index of agreement D of 0.83. Results of the model show that there is strong agreement between the simulated and observed data.  The resulting surface runoff, �Fig. 8� over the time series considered indicates steady increase of surface runoff. There is a sharp shift of the hydrology of the basin from normal sub surface flow to rapid overland flow due to urbanization which is a trigger for flush flood in the basin in recent history.
	

ConclusionsThe analysis of the EuDASM image and LandSat TM images of 1964 and 2007 respectively, revealed that land use and land cover of the Odaw catchment has changed significantly over the years. Over the 43 years, changes in land use and land cover of the Odaw catchment were found to be related to population growth and urbanization with a higher rate of surface imperviousness. In the context of hydrologic analysis runoff magnitudes are estimated as a result of precipitation. These estimates consider processes in a watershed such as land use functions that transform precipitation to runoff and further transport water through the drainage basin. Based on the trend of land use changes detected in this study, there is increased in overland surface water flows due to the increasing curve number CN. Hence runoff was found to be strongly related to the land use and land cover change. The SCS-CN model revealed that the runoff is more sensitive to the changes in the trend of land use than the ratio of initial abstraction to the maximum water potential of the basin. The ratio of the initial abstraction to maximum water potential was optimized as 0.09. It is therefore needful to improve on the basin storage holding capacity as a means to offset the occurrence of increasing overland surface water flows. These results constitute a helpful document for hydrologic modeling that allows the calculation of flow profiles and estimation of runoff functions that affects the hydrology of related urban basins.

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36. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: Part I � A discussion of principles. Journal of Hydrology, 282-290.










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