Dynamics of land use and land cover changes and implications on river flows in Simiyu River catchment, Basin in Deogratias M.M. Mulungu1 and Japhet J. Kashaigili2

1University of Dar es Salaam, College of Engineering and Technology, Department of Water Resources Engineering, P.O. Box 35131, Dar es Salaam, Tanzania 2Faculty of Forestry and Nature Conservation, Sokoine University of Agriculture, P.O. Box 3013 Morogoro, Tanzania

Abstract

The Simiyu River catchment in the Lake Victoria Basin is important for agriculture, fishing and livestock keeping. However, increased pressure on the land due to rise in population, expansion in agriculture to include the wetland areas, are threatening the sustainability of the catchment resources. Anecdotal evidence has it that the catchment has tremendously changed and the changes have impacted on the water resources of the catchment. Nevertheless, very little is known about the quantities of the changes and the implications of the changes on river flows. Therefore, a study was conducted to quantify the land-use and land-cover changes for the 1980s and 2000s and subsequently investigated the implications of the detected changes on flow regimes of the Simiyu River. Remote sensing and GIS techniques were used to inventory temporal changes of land use and land cover changes in the watershed. The ground truth data were collected using a GPS and used in verification of the land covers. The post-classification comparison method was used to assess land use and cover changes followed by the estimation for the rate of change. Hydrological data were analyzed to reveal the alterations and trends for two time periods; pre-1988 and post-1988. The study revealed the transformation of larger area of the catchment to agriculture with an annual increase of 29.41%, followed by forest (+4.69%) and settlement (+4.68%). The wetland area was found to decline at a rate of -3.33% annually and the grassland at -4.06%. The river flows were found to be variable within and between the years, and sensitive to land use and land cover changes. There was a slight shift of the timings for peak flows in the recent period than the former and declining magnitudes of mean and base flows. The study concludes that the modification of the land use and cover has resulted in changes in temporal distribution of catchment runoff. The study highlights the importance of remote sensing in understanding the land use and land cover dynamics and the implications of changes on catchment runoff for informed decisions on the sustainability of the catchment resources.

Keywords: Agriculture, Land use and land cover change, Landsat imagery, Lake Victoria Basin, river flows, Simiyu River catchment, Wetland, Tanzania

1. INTRODUCTION

Understanding the influence of land use and land cover change on river flow regimes is important for sustainable catchment management (Kashaigili, 2008). According to Di Gregorio & Jansen (2000), land cover describes the physical states of the land surface including cropland, forest, wetlands, pastures roads and urban areas, whereas land use relates to the manner in which these biophysical assets are used by humans (Cihlar and Jansen, 2001). Since land is featured with spatial extent, spatial data and mapping techniques are used to show different land uses and cover in geographical settings. According to Mulungu and Munishi (2007), spatial data is very valuable for providing cost-effective data input and estimating parameters for hydrological model. The spatial data is therefore important for understanding the behaviour of the earth and its hydrological system, and for identification of priority areas for management.

In order to understand recent changes and to generate scenarios for predicting future modifications of the earth system, the scientific community needs quantitative, spatially explicit data on how land cover and use have changed over the years, and how it will be changed in the future. According to Lambin (1997), land use and land cover change analysis is an important tool to assess global change at various spatial–temporal scales. As such data on land cover changes need to be relevant for local decision- makers (Lambin and Geist, 2001).

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Dynamics of land use and land cover changes and implications on river flows in Simiyu River catchment, Lake Victoria Basin in Tanzania

According to Zhou et al., (2008) land cover change often reflects the most significant impact on the environment due to human activities or natural forces and that remote sensing can be an appropriate tool for getting wide impression on land cover change. It is now widely accepted that information generated from remotely sensed data is useful for planning, and decision making. Considering the rapid changes in land cover occurring over large areas, remote sensing has become an essential tool for data generation and monitoring. According to Ogutu et al.,

(2005) humans have degraded most of the services and products from land and that there is a need for undertaking a research to generate data for objective decision on sustainable utilization of land resources in the Lake Victoria Basin.

Against this background, this paper presents a study undertaken to investigate the influence of land use and land cover changes on the hydrological regimes of the Ndagalu and Sayaga Rivers in Simiyu River Catchment. The catchment is part of the Lake Victoria Basin and it is important for agriculture, fishing and livestock keeping. However, increased pressure on land due to rise in population, expansion in agriculture to include the wetland areas, are threatening the sustainability of the catchment resources. To-date there is very limited information on the dynamics of land use and cover in the catchment that could inform planning for sustainable management of the catchment resources. This study therefore contributes to the efforts towards sustainable resource utilization, monitoring, planning and management in the catchment. Previous studies on land cover and land use in the catchment focused only on portions of the catchment near the Lake Victoria and assessed erosion hazards and analysed non-point pollution sources, respectively (Yanda et al., 2001; Tamatamah, 2002). In both studies, land use was identified as a major factor influencing the land degradation and pollution yield respectively in the catchment. Yanda et al., (2001) indicated that the main driver of human-induced vegetation changes was deforestation, caused by the expansion of agro-pastoralism. Tamatamah (2002) indicated that the critical non-point sediment and phosphorus contributing areas were identified and closely linked to population pressure, cultivation, deforestation and the consequent soils erosion.

2. MATERIALS AND METHODS

2.1 Description of the Study Area

The Simiyu River catchment is located southeast in the Lake Victoria Basin, between 33o 15’ – 35o 00’ E and 2o 15’ – 3o 30’ S and covers an area of about 11,000 km2 to the outfall into the lake (Figure 1). The Simiyu River drains from the Serengeti National Park plains and the Maswa Game Reserve to the Lake Victoria. Duma and Simiyu Rivers are the main tributary rivers, which form the Simiyu River catchment. The catchment is important for agricultural and other economic activities such as fishing and livestock in the rural areas. Farmers in the catchment rely much on rain-fed agriculture as there is very little irrigated agriculture. The main crops grown in the catchment include maize, beans, sorghum, millets, rice, cotton, sweet potatoes, cassava and chickpeas. The wetland area on the Simiyu River valley near the river outfall to the Lake Victoria is a flat land area with mbuga clay fertile soil. The major crops grown in the area are maize, banana, cotton, rice, sweet potatoes and vegetables. The vegetables include cabbage, tomatoes, onions and watermelon. Among the crops grown in the Simiyu catchment, rice, cotton and vegetables are cash crops while maize can be a cash crop during periods of food shortage.

Socio-economic practices are characterized by activities such as subsistence farming, shifting cultivation and extensive grazing of cattle, which have significant impact on the land cover or land use dynamics. Tamatamah (2002) noted that with population densities approaching 150/km2, the Simiyu catchment has become one of the most highly populated areas in the Lake Victoria basin. In addition, the dense human population matched with large cattle stocks, which is mainly kept in an extensive open-range. These agriculture and livestock grazing activities give enormous pressure on the land, leading to deforestation and soil erosion. Furthermore, Yanda et al., (2001) indicated that the other important factors of land use changes in the Lake Victoria basin areas include charcoal making, timber harvesting, large-scale fuel wood harvesting for sale and fish processing. The Simiyu catchment experiences semi-arid climatic conditions with erratic rainfall and sometimes relatively low annual rainfall in a year comparing to other years or region of higher rainfall, making it a prone-zone to agricultural drought. The occurrence of agricultural droughts results into decreased or poor agricultural productions. Poor agricultural production and lack of surplus are considered to be the main grounds for

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Dynamics of land use and land cover changes and implications on river flows in Simiyu River catchment, Lake Victoria Basin in Tanzania increased poverty in the catchment due to reliance of most people on agriculture as their major means of production. Slumping of agricultural gains is linked to increase in poverty and hunger for the population of around 1.3 million (URT, 2002) residing in the catchment area. Moreover, water resources in the catchment are now under great demand due to recurrent droughts, increase in population and increase in economic activities as a result of the increased population. This has led to increasing need for capacity and integrated efforts geared for land resources management and socio- economic development of the catchment areas.

2.2 Image Selection and Acquisition

The selection of appropriate imagery acquisition dates is as crucial to the change detection method as is the choice of the sensor(s), change categories, and change detection algorithms. In consideration of cloud cover, the seasonality and phenological effects (e.g. Jensen, 1996) images listed in Table 1 were selected for image processing. The target was images acquired during the dry season (July-October) with minimum cloud cover. The required images were however not readily available as required. As a consequence, images acquired during the January – February (dry spell) and May (long rains) were used. Nevertheless, detailed training of the site had to be done and GIS tools such as Area of Interest (AOI) were applied using visual analysis, reference data and local knowledge to minimize the seasonality effect.

Table 1: Selected image data

Sensor Path Row Date of acquisition Spatial resolution Provider (m) TM 170 62 05/03/1985 28.5 GLCF ETM+ 170 62 12/05/2001 28.5 GLCF TM 169 62 16/02/1987 28.5 GLCF ETM+ 169 62 12/02/2000 28.5 GLCF TM = Thematic Mapper, ETM+ = Enhanced Thematic Mapper Plus, GLCF= Global Land Cover Facility

2.3 Image Pre-processing

The methods for the images analysis required the use of both visual and digital image processing. The processing involved image rectification/georeferencing and co-registration and image enhancement. Prior to image processing, images layers/bands were imported and layer stacked to full scene. All image processing and subsequent image analysis were carried out using ERDAS Imagine Software Version 9.2.

2.3.1 Image rectification and Geo-referencing

To ensure accurate identification of temporal changes and geometric compatibility with other sources of information, images were geometrically rectified and registered to the UTM map coordinate system (i.e. UTM zone 36 South, Spheroid Clarke 1880, Datum Arc 1960). Most of the images were already rectified and available as geo-cover datasets from the GLCF. For those which needed rectification, image to image rectification was carried out with an overall RMS-error of less than 1.5 pixels. Image rectification was undertaken using a 2nd order Polynomial transformation and nearest-neighbourhood interpolation. In order to examine how well images were rectified, temporal images were overlaid on the same window and zoomed in to various features at multiple locations around the scenes. The “swipe” command within the ERDAS Imagine software aided checking the conformity of the co- registration. Following to that a mosaic of images covering the entire Simiyu River catchment using images of 1985 and 1987 (Figure 1), was created to evaluate the accuracy of the rectification from one scene to another. A shapefile for Simiyu catchment was then overlaid to confirm the catchment boundary.

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Dynamics of land use and land cover changes and implications on river flows in Simiyu River catchment, Lake Victoria Basin in Tanzania

Figure 1: Map of Tanzania and image mosaic (p169r62 and p170r62) with overlaid Simiyu River catchment

2.3.2 Image enhancement

In order to reinforce the visual interpretability of images, a colour composite (Landsat TM bands 4, 5, 3 ) was prepared and its contrast was stretched using a Gaussian distribution function. Furthermore, a 3 x 3 high pass filter was applied to the colour composite to further enhance visual interpretability of linear features, e.g. rivers, roads and patterns such as cultivation.

2.4 Ground Truthing and Image Classification

The field work was conducted during the dry season in October 2006, to establish ground truthing data for the verification and modification of the land covers of the classified images. GPS was used to locate sampled land cover observations while digital camera recorded photos on physical features about the areas. All sampled GPS points were booked and photograph numbered. Key informants were also involved to give some information on land cover/ use particularly for the past periods. Also, topographic maps covering the entire Simiyu River catchment provided some ground truth information about the catchment. Supervised classification, using Maximum Likelihood Classifier (MLC), was utilized for image classification. Supervised classification process involved selection of training sites on the image, which represent specific land classes to be mapped. Training sites are sites of pixels that represent specific land classes to be mapped (ERDAS, 1999). They are pixels that represent what is recognized as a discernable pattern, or potential land cover class. The training sites were generated by on-screen digitizing of selected areas for each land cover class identified on colour composite. Training was an iterative process, whereby the selected training pixels were evaluated by performing an estimated classification (ALARM command). Based on the inspection of ALARM results, training samples were refined until a satisfactory result was obtained.

2.5 Post-processing of Classified Images

Classified images were recorded to respective classes i.e. wetland vegetation, forest, built up area, bare land, woodland, burnt area, etc. Following the recoding, images were filtered using a 3 x 3 majority- neighbourhood filter. Filtering was done in order to eliminate patches smaller than a specified value and replace them with the value that is most common among the neighbouring pixels. A minimum of 15 pixels for Landsat TM and ETM+ was used. Since the pixel resolution for Landsat TM and ETM+ was 28.5 m, 15 pixels approximates to 1.2 hectares. Therefore, the minimum mapping unit for the final map was 1.2 hectares. A mosaic operation was performed to multiple classified images to produce one change map for all the Simiyu River catchment.

2.6 Change Detection Analysis and Estimating Rate of Change

Change detection is a very common and powerful application of satellite based remote sensing. Change detection analysis entails finding the type, amount and location of land use changes that are taking place (Yeh et al., 1996). Various algorithms are available for change detection analysis. They can be grouped into two categories namely a) Pixel-to-pixel comparison of multi-temporal images before image classification, and b) Post-classification comparison. Details on these methods can be found in various literatures (e.g., Jensen, 1996; ERDAS, 1999). In this work, a post-classification comparison

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Dynamics of land use and land cover changes and implications on river flows in Simiyu River catchment, Lake Victoria Basin in Tanzania method was used to assess land use and cover changes. The method has been found to be the most suitable for detecting land cover change (Wickware and Howarth, 1981). The approach identifies changes by comparing independently classified multi-date images on pixel-by-pixel basis using a change detection matrix (Jensen, 1996; Yuan and Elvidge, 1998). The matrix analysis produces a thematic layer that contains a separate class for every coincidence of classes in multi-date dataset. Although, the use of a change detection matrix provides detailed from-to information on the nature of change, mis-classification and mis-registration that may be present in each classified image may affect the accuracy of the results. Therefore, accurate classifications are imperative to ensure precise change detection results (Foody, 2001). The estimation for the rate of change for the different covers was computed based on the following formulae (Kashaigili et al., 2006):

Areai year x − Areai year x+1 (1) % Cover change = n x 100 ∑ Area i=1 i year x

Area − Area Annual rate of change = i year x i year x+1 (2) t years

Area − Area % Annual rateof change = i year x i year x+1 x100 (3) Areai year x x t years

Where: Area i year x = area of cover i at the first date, Area i year x+1 = area of cover i at the second date, n ∑ Area = total cover area at the first and i=1 i year x

t years = period in years between the first and second scene acquisition dates

2.7 Rainfall and River Flows Analysis

To understand the implications of land use and land cover changes on hydrological flow regimes in Simiyu River Catchment, analysis of flows and rainfall data was done. The available daily flow data at the two gauging stations (Ndagalu and Sayaga) spanning from 1970 to 1996 were used to determine the flow characteristics. The rainfall and river flow data were collected from the Lake Victoria Basin Water Office in Mwanza. Two time windows were considered namely 1970-1987 and 1988-1996, representing different characteristics related to land use and land cover change in the catchment. The time series analysis of daily flow data was conducted and flow duration curves developed, from which indices of flows were extracted and compared for the two time periods.

3. RESULTS AND DISCUSSION

3.1 Land Cover Maps

The land cover maps for 1985/87 and 2000/01 are presented in Figure 2. Generally, a large area of the geographical subset study area of 1,834,208.5 ha is a built up area with mixed farming activities.

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Dynamics of land use and land cover changes and implications on river flows in Simiyu River catchment, Lake Victoria Basin in Tanzania

(a) (b) Figure 2: (a) &(b). Land cover/use map for Simiyu River catchment and neighbouring areas during for the period 1985/1987 and 2000/2001 respectively

3.2 Detected Changes

Table 2 is a summary showing the coverage of each land cover/ use class in the 1980s and 2000s including the area, percentage area change and the rates of changes between the two periods for the Simiyu catchment. The total area of settlement and bare land which occupied 288967.4 ha (26.2% of the total area for Simiyu River catchment of 1101755.2 ha) in 1980s, increased to 491931.3 ha (45%) in 2000s, indicating an increase in settlement and bare land area of about 202963.9 ha (18.4%). The wetland vegetation declined from 4.4% (of total area cover in 1985/87) to 2% in 2000/01 indicating a 2.2% decrease in wetland area between the two periods for the entire Simiyu catchment area. Likewise for other covers, either the area increased or decreased between the two periods of consideration.

From Table 2 it is clear that cultivation class experienced significant annual increase (+29.4% per year) between 1980s and 2000s assuming a linear increase. The wetland vegetation cover consistently declined between the periods at an annual rate of -3.3%. The forest area increased at a rate of +4.7% per year while the woodland area declined at -0.3% per year. The decline in wetland areas could be attributed to increased cultivation activities in wetlands which are also reflected in increased area under settlements between the periods under consideration. The increase in forest area could be due to increased soil conservation activities that involved afforestation (tree planting) as revealed by key informants. In Magu area for example, people have planted eucalyptus trees as a way of curbing fuel wood deficiency in the district. On the other hand, the decline in woodland area could be attributable to Sukuma agro-pastoral type of farming that involve tree cutting and land clearing. Other covers e.g. bushed grassland and grassland respectively declined at an annual rate of -0.2% and -4.1% respectively between 1980s and 2000s. These changes might be attributable to increased agricultural activities in the area including fire. The clouds and cloud shadow do not portray real change instead they indicate cloud coverage between the two periods under consideration. For example in the 1980s, there were no cloud shadows over Simiyu River catchment unlike the latter period (2000/01) where they occupied an area of 1327.8 ha. Likewise, there was no detected burnt area in the former period (1980s) unlike the latter (2000s) where the extent of burnt area was 11979.6 ha.

A simple analysis based on subtracting areas may often be misleading and in principle, should be supplemented by an analysis of change detection matrix (Mbilinyi, 2000). To examine in more detail how the land cover classes changed between 1985/87 and 2000/01, the land cover transition matrix (change detection matrix) was calculated (Table 3). The land cover transition matrix (Table 3) shows the area conversions between land-cover classes in 1985/87 and 2000/01. The numbers in brackets indicates the cover area which remained unchanged between 1985/87 and 2000/01, while others indicate the flow of covers or covers that changed to another cover category. It is important to note that all land cover categories changed but with varying magnitudes. For example, there was a transition from wetland vegetation (WV) cover to cultivation, settlement / bare land, bush land, burnt area, grassland, etc.

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Dynamics of land use and land cover changes and implications on river flows in Simiyu River catchment, Lake Victoria Basin in Tanzania

Table 2: Cover area and the percentage changed between 1980s and 2000s

Annual % 1985/ Land use/cover % cover 2000/ 2001 % cover Change % rate of annual 1987 class coverage Area (ha) coverage Area (ha) change change rate of Area (ha) (ha) change Open water 19050.2 1.7 2956.8 0 -16093.4 -1.5 -1072.9 -5.6 Forest 5808.5 0.5 9894.1 1 4085.6 0.4 272.4 +4.7 Wetland -1624.2 vegetation/Marsh/ 48720.9 4.4 24357.5 2 -24363.4 -2.2 -3.3

Bog Woodland 231054.5 21.0 219164.7 20 -11889.8 -1.1 -792.7 -0.3 Bushed grassland 149549.2 13.6 145545.5 13 -4003.7 -0.4 -266.9 -0.2 Grassland 328316.3 29.8 128207.9 12 -200108.4 -18.2 -13340.6 -4.1 Settlement/bare 288967.4 26.2 491931.3 45 202963.9 18.4 13530.9 +4.7 land Cultivation/paddy 4683.4 0.4 25347.0 2 20663.6 1.9 1377.6 +29.4 fields Rock outcrop 9265.3 0.8 18163.8 2 8898.4 0.8 593.2 +6.4 Black cotton soil 16145.1 1.5 17347.5 2 1202.5 0.1 80.2 +0.5 Burnt area/burn 0.0 0.0 11979.6 1 11979.6 1.1 798.6 - scar *Clouds 0.0 0.0 1159.8 0 1159.8 0.1 77.3 - *Cloud shadow 0.0 0.0 1327.8 0 1327.8 0.1 88.5 - *Mountain 194.3 0.0 4371.9 0 4177.4 0.4 278.5 +143.3 shadow Total 1101755.2 100 1101755.2 100 * These classes do not portray the actual changes, but indicate the effect of weather condition and the angle of image acquisition. The presence of clouds and shadows limit the interpretability of the areas where they occur.

3.3 Variations on Detected Changes and Interpretations

Discrepancies or variations on results from change detection analysis are inevitable and these could impair the interpretability for the detected changes (Kashaigili et al., 2006). In this study, some variations on the detected changes were noted although the Overall Classification Accuracy was 85.3%. For example, scrutinizing the change detection matrix (Table 3), one identifies that some of the changes were unrealistic. For instance, wetland vegetation changing to rock outcrop is very unlikely. The different plant phenological effects are related to the season to which an image is acquired on the ground by the satellite. In this study, the wet season images have been used; therefore there is no wonder that these variations could result to cover over or underestimations because of seasonal effects. Studies conducted somewhere have shown that the dry period is the most desirable period for image change analysis. For example, Burns and Joyce (1981) noted that selecting the driest period of the year for change analysis will enhance spectral separability and yet minimize spectral similarity due to excessive wetness prevailing during other periods of the year. The wet season spectral separability, which is responsible for class assignment, becomes somewhat difficult and may result into misclassification of some of the classes, which results into under-or over-classification. The fieldwork for collection of ground truth points was done at the peak of the dry season while the available and used images were acquired in the wet season. In any circumstance, such seasonal differences could affect their use during class verification.

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Dynamics of land use and land cover changes and implications on river flows in Simiyu River catchment, Lake Victoria Basin in Tanzania

Table 3: Change detection matrix for the period 1985/87 and 2000/01

Cover in 2000/2001 (ha) Total OW FO WV WO BG GR SET CU RO BCS BA CL CLS MS (1980s) OW (1232.9) 4.4 1045.8 723.3 1898.4 2892.6 4990.2 719.7 1015.8 3771.0 628.3 48.5 78.7 0.7 19050.2 FO 0.6 (705.0) 317.9 3338.9 463.1 161.2 187.3 117.1 146.3 36.7 8.9 0.0 0.0 325.3 5808.5 WV 331.1 560.0 (4110.3) 9133.7 6533.3 7610.0 11827.5 1462.5 1868.7 3336.8 1734.2 62.3 65.5 85.2 48720.9 WO 36.1 6451.9 8890.2 (115797.1) 29496.6 43852.5 17086.2 3849.4 1718.6 393.9 423.5 2.7 14.8 3040.9 231054.5 BG 89.9 38.6 1226.3 14356.8 (15891.7) 19532.6 91558.4 1204.2 2997.4 1289.9 898.4 204.0 219.3 41.7 149549.2 GR 666.3 917.4 5334.9 51553.3 68267.9 (37607.7) 133147.1 13415.9 5464.3 4499.5 6542.2 113.5 155.7 630.6 328316.3 SET 528.4 28.2 1228.9 16493.5 17720.7 13075.7 (229040.2) 1794.3 3578.2 2954.6 1012.7 716.1 784.5 11.5 288967.4 CU 9.4 0.3 151.1 603.4 730.3 484.7 435.6 (1645.8) 115.7 158.6 346.3 0.1 2.1 0.0 4683.4 RO 40.0 288.4 277.9 2438.0 2022.1 815.3 1652.4 469.9 (893.6) 143.4 98.1 2.0 6.0 118.1 9265.3 BCS 22.0 777.9 1774.3 4690.7 2516.8 2173.7 2006.3 668.2 364.1 (763.0) 286.9 10.6 1.2 89.2 16145.1 Cover in 1985/1987 (ha) 1985/1987 in Cover BA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 (0.0) 0.0 0.0 0.0 0.0 CL 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 (0.0) 0.0 0.0 0.0 CLS 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 (0.0) 0.0 0.0 MS 0.0 121.9 0.0 36.1 4.6 1.9 0.0 0.0 1.1 0.0 0.0 0.0 0.0 (28.7) 194.3 Total (2000s) 2956.8 9894.1 24357.5 219164.7 145545.5 128207.9 491931.3 25347.0 18163.8 17347.5 11979.6 1159.8 1327.8 4371.9 1101755.2

OW=Open water, FO=Forest, WV=Wetland vegetation, WO=Woodland, BG=Bushed Grassland, GR=Grassland, SET=Settlement/ bareland, CU=Cultivation/paddy fields, RO=Rock outcrop, BCS=Black cotton soil, BA=Burnt area/burn scar, CL=Clouds, CLS=Cloud shadow, MS=Mountain shadow NB: Numbers in brackets (in diagonal) indicate the cover class area which remained unchanged between the two periods

3.4 Rainfall and River Flows

Mean monthly rainfall Figure 3a, b and c represents the mean monthly rainfall at the three selected rainfall stations in the Simiyu River catchment. Generally, the monthly rainfall at the three stations do not show much variations over the periods, indicating that there has not been much changes in rainfall received in the catchment.

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Dynamics of land use and land cover changes and implications on river flows in Simiyu River catchment, Lake Victoria Basin in Tanzania

(a) (b)

(c) Figure 3: a, b, c. Mean monthly rainfall at three representative rainfall stations in Simiyu catchment

Seasonal Daily River flows and Flow duration curves Figure 4 present the seasonal daily river flow variations at Ndagalu and Sayaga gauging stations. The two stations that are at some 12.6 km apart indicate some variations in hydrological flow response. For any rainfall event, there is generally fast peaking and sharp recession in the later period (1988-1996) than in the former (pre-1988). The base flow at Sayaga (Fig. 4b) is very highly modulated in the later period than the former period. This is also supported by the mean monthly flows (Figure 5a) which reveals a consistent decreasing in amount of river flows in the later period unlike the former. However, it is important to note that there is much flow generated at the onset of the short rains (Oct/Nov-Jan) for the later period unlike the former. Figure 5b presents the flow duration curves (FDC) of one day duration at Sayaga gauging station while Table 4 presents the low flow indices at Ndagalu and Sayaga gauging stations for the different periods. From Fig. 11, the FDC curves confirm the progressive and significant decline in flows. Between the pre-1988 and 1988-1996 windows, the low flow indices changed consistently (Table 4). For example, at Sayaga gauging station, the Q50 Q75, Q90 and Q95 decreased from 12.5 m3s-1, 6.2 m3s-1, 2.5 m3s-1 and 1.2 m3s-1 to 9.8 m3s-1, 4.9 m3s-1, 1.7 m3s-1 and 0.7 m3s-1, respectively. Likewise the same pattern was observed at Ndagalu gauging station (Table 4).

(a)

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Dynamics of land use and land cover changes and implications on river flows in Simiyu River catchment, Lake Victoria Basin in Tanzania

(b) Figure 4: (a) & (b). Seasonal daily river flow variation during Pre-1988 and 1988-1996 periods at Ndagalu and Sayaga gauging stations respectively

(a) (b)

Figure 5: (a) Mean monthly flows at Sayaga gauging station (112022) for the (Pre-1988 and 1988- 1996 periods), (b) 1-D Flow duration curves at Sayaga gauging station (112022) for the (Pre-1988 and 1988-1996 periods)

Table 4: Low flow indices at Ndagalu and Sayaga gauging stations for the different periods

Station code Period Low Flow Index (Name) (Cumecs)

Q50 Q75 Q90 Q95 Q99 112012 1970-1987 2.6 0.5 0.2 0.1 0.0 (Ndagalu) 1988-1996 0.6 0.0 0.0 0.0 0.0 112022 1970-1987 12.5 6.2 2.5 1.2 0.0 (Sayaga) 1988-1996 9.8 4.9 1.7 0.7 0.0

3.5 Implications of Land Use and Land Cover Changes on River Flow Regimes

The study has revealed a clear link between the changes in land use and land cover, and the changes in hydrological regime of the Simiyu river catchment. As a result of land use and land cover changes, the hydrological regime of the Simiyu catchment has changed in terms of runoff magnitude and distribution. There is more runoff generation at the onset of the rains in the later (1988-1996) period and less sustained flows during the low flows period. Such findings coincide with the other studies done elsewhere (e.g. Kiersch, 2000; Allan, 2004; Kashaigili, 2008). According to Kiersch (2000), the

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Dynamics of land use and land cover changes and implications on river flows in Simiyu River catchment, Lake Victoria Basin in Tanzania impacts of land use practices on surface water can be two-fold: (i) on the overall water availability or the mean annual runoff, and (ii) on the seasonal distribution of water availability.

4. CONCLUSION

This study investigated the land use/ cover changes of the Simiyu River catchment and the neighbouring areas. The analysis involved Landsat image scenes acquired in 1985/87 for the former period and the 2000/01 for the latter period. The study revealed the changes in land use and land cover for the two investigated periods. It has revealed that the catchment has experienced significant changes in land use, with larger areas being transformed to agriculture and settlement. Notably, a larger part of the wetlands and woodlands have now been converted to agriculture and settlement. The change in hydrological regime in Simiyu catchment has been revealed with much changes occurring in the later period than the former. The study concludes that the modification of the land use and cover has resulted in changes in magnitude and temporal distribution of runoff within the catchment. Furthermore, the study highlights the importance of remote sensing in understanding the catchment resources dynamics. The information presented in this paper could be used to inform various stakeholders on the need for sustainable catchment management.

5. ACKNOWLEDGEMENTS

The authors acknowledge the financial support from the University of Dar es Salaam, Tanzania through the SIDA/SAREC Core Support Program and the Department of Water Resources Engineering for facilitation of this research.

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AUTHORS BIOGRAPHY

Dr. Deogratias M.M. Mulungu holds a PhD in Engineering Hydrology. He is a Lecturer at the College of Engineering and Technology of the University of Dar es Salaam and experienced researcher in the fields of hydrological processes, hydrological modelling and water resources management.

Dr. Japhet J. Kashaigili holds a PhD in environmental hydrology and natural resources assessment. He is a Senior Lecturer at the Sokoine University of Agriculture and experienced researcher in the fields of environment, water resources, irrigation and natural resources management.

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