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Land Cover Changes and Its Effects on Streamflow in the Malewa River

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Land Cover Changes and Its Effects on Streamflow in the Malewa River E. Afri. Agri. For. J (Pg. 41 -52 Special Issue) LAND COVER CHANGES AND ITS EFFECTS ON STREAMFLOWS IN THE MALEWA RIVER BASIN, KENYA *M. K. Cheruiyot1,G. Gathuru2, J. Koske2 and R. Soy3 1WWF International – Africa, Nairobi, Kenya;2Kenyatta University, Nairobi, Kenya; 3Directorate of Resource Surveys and Remote Sensing, Nairobi, Kenya ABSTRACT forests contribute to water yield. The lack of clarity is informed by several forest-interaction studies (Beck Vegetated landscapes are transformed by both natural et al., 2013; Ellison et al., 2012; Zhou et al., 2010; and human causes. This is thought to influence river Silveira et al., 2006) under different contexts suggesting flow regimes. It is argued that restored and reforested inconclusiveness on the relationship between forests and landscapes increase stream flow. However, studies done stream flows. It is argued that forests at larger spatial to date have been inconclusive on whether or not trees scales contribute to increased evapo-transpiration and on restored or reforested landscapes increase stream flow. precipitation (Ellison et al., 2012). Also, while forests This study aimed to examine the effects of land cover may be net consumers of water and competitors for changes on streamflow of the Malewa River Basin in other downstream users, reduced forest cover could Kenya. Satellite imagery based spatial change detection increase stream flows (Price et al., 2010). Generally, using ArcGIS 10.1 and ERDAS IMAGINE software was land use changes and their associated effects are known deployed to estimate the land cover changes. Based on to impact the hydrology of the catchment area (Tang et projected land cover change data, a multiple regression al., 2005; Foley et al., 2005; Ott and Uhlenbrook, 2004; technique was used to establish the relationship between Bronstert et al., 2002). Vegetation cover is thought to land cover and streamflow. The results show that at increase the capacity of catchments, conserve moisture Gauge 2GB01, area under wetland significantly predicted and increase water yield (Lal, 1997). There seems to be a stream flows (b=0.134, t(488) =1.978, p=0.049), with an relationship between altered flows and ecological change 2 overall model (R =0.018, F(3, 488)=2.976, p=0.031). (Poff et al., 1997); however no evidence existed to show Area under grassland (b=0.108, t(488)=2.325, p=0.02), that ecological change was dependent on hydrological shrubland (b=0.112, t(488)=1.976, p=0.049) and change (Poff and Zimmerman, 2010). Surface runoff amount of rainfall (b=0.533, t(488)=14.048, p=0.000) and river discharge are generally thought to increase combined significantly predicted stream flows. Rainfall with clearance of natural vegetation, especially forests. alone significantly predicted stream flows (b=0.531, This was evidenced, for example, in the Tocantins River t(488)=13.885, p=0.000). Overall, the gains in forest Basin in Brazil (1960-1995) where an approximately restoration did not specifically influence streamflow 25% recorded increase in river discharge was attributed except in combination with other vegetation and rainfall. to expanding agriculture and not a change in precipitation There is need to increase soil cover rather than woody (Costa et al., 2003). Elsewhere, stream flows of once biomass alone in the regulation of stream flows. A degraded areas under large-scale land rehabilitation systematic response to address the drivers of change in showed improved base flows (Wilcox and Huang, 2010). land cover is also needed. On the contrary, findings from 12 meso scale catchments (23-346 km2) in the island of Puerto Rico do not show Key words: landscape, streamflow, restoration, land influence of changes in urban or forest cover on stream cover flow trends (Beck, 2013). INTRODUCTION In the Mara River, Kenya, it was shown that higher flood Vegetated landscapes are thought to influence river peaks and faster travel times were experienced in an area flow regimes, but there is lack of clarity about how that had undergone increased land use pressure (Mutie et al., 2006). In the Nzoia river catchment, it has been observed that forests reduce runoff with increased flows *Corresponding author: [email protected] in croplands compared to forests. The findings show that as a result of an increase in agricultural area of between CHERUIYOT, GATHURU, KOSKE AND SOY 39.6 and 64.3% and reduced forest land from 12.3 to 7.0% Nagelhout, 2001). The soils are prone to erosion and (1973-2001), runoff increased by 119% (1970-1985). compaction (Kiai and Mailu, 1998). Forests and cropland The authors noted that climatic factors being constant, dominates the upper catchment (the Nyandarua range) land cover changes was responsible for the difference while livestock grazing is done at the lower catchments in run-off ranging from 55-68% (Githui et al., 2009). In (Muthawatta, 2004). another study in this catchment, arising from agricultural expansion, stream flow was found to increase during rainy Study design seasons but decreased during the dry seasons. Stream flow The study area was sub-divided into three sub-catchments, generally increased with increase in forest cover. However, namely Turasha (Sub-catchment I), Upper Malewa (Sub- when the cover reduced to almost zero, increased peak catchment II) and Malewa (Sub-catchments II and III and mean discharge was noted (Odira et al., 2010). combined). Sub-catchment I was mapped to Gauge Despite the extensive literature on responses of baseflow 2GC04, Sub-catchment II to 2GB0708 and Sub-catchment and recharge to various human impacts (Price, 2011) II and III combined to Gauge 2GB05. The entire basin these findings are inconclusive. Effects of regenerating consisting of the three sub-catchments was mapped to forests on stream flow is still little known. This study was Gauge 2GB01. designed to address the problem of limited understanding Data collection and analysis on how forests and trees sustain stream flow in a human modified landscape. It contributes to a body of knowledge Satellite imagery from Landsat MultiSpectral Scanner on land cover classes – water yield relationships. The (MSS) (1973), Landsat Thematic Mapper (TM) (1986) objective of this study was to analyse the land cover and Enhanced Thematic Mapper Plus (ETM+) (2000) changes and its effects on streamflows in the Malewa were obtained from the River Basin in Kenya. Landsat database (orthorectified archives) (NASA, MATERIALS AND METHODS 2015). These images were geo-processed using ERDAS imagine 2015 and ArcGIS 10.1 software. SPOT image Study area from Astrium was acquired courtesy of WWF Kenya The Malewa River Basin (1,760 km2) is located in Kenya office. UTM Projection Zone 37N and WGS 84 Datum (See Figure 1) and itcovers Nakuru and Nyandarua were adopted in the registration procedures. DeltaCue Counties. The Malewa River discharges about 153 million software was used to perform image registration (ERDAS cubic metres (MCM) of water per annum (Arwa, 2001). Inc, 2008). Threshold based segmentation technique The river has a dendritic drainage system, with several where a multilevel image is converted into a binary image streams (including Turasha, Kitiri, Mkungi, Wanjohi and (Telgad et al., 2014) was applied. Image processing Malewa) emerging from the upper catchment. Rainfall and enhancement was done using ERDAS imagine ranges between 600 and 1,700 mm, with the Kinangop 2015. An object-based classification using a supervised plateau experiencing a yearly rainfall ranging from 1,000 maximum likelihood classification technique was used. to 1,300 mm (Becht and Higgins, 2003). The climatic A classification scheme based on Anderson et al. (1976) conditions mirror that of the semi-arid areas, with bi-modal was adopted, with six distinct classes generated, namely rainfall distribution: longer rainy season (March to May) cropland, forestland, grassland, shrubland; wetland and and short rainy season (October to November, February, settlement. Interpreted raster was then converted into July and December) as described by Kamoni (1988). The polygons using conversion tool in ERDAS imagine. The potential evaporation is about twice the annual rainfall normalized difference vegetation index (NDVI) algorithm (Farah, 2001). The mean annual temperature ranges (Rouse et al., 1973) was used to detect vegetation health. between 16 oC and 25 oC. The daily temperatures range Daily stream flow data for gauges 2GB01, 2GB05, from 5 oC to 25 oC (Republic of Kenya, 2014). Soils have 2GB0708 and 2GC04 (see Figure 1) was sourced from the been influenced by extensive relief variation, volcanic Water Resources Management Authority. Monthly rainfall activity and underlying bedrocks (Sombroek et al., data for six stations (9036243 - Dundori Forest Station, 1982); and developed from lacustrine deposits, volcanic 9036029 - Kwetu farm, 9036002 - Naivasha Water Bailiff, and lacustrine-volcanic basements (Girma et al., 2001; 42 Land Cover Changes and Its Effects on Streamflows in the Malewa River Basin, Kenya Figure 1.Study area showing location of the gauge and rainfall stations 43 CHERUIYOT, GATHURU, KOSKE AND SOY 9036081 - National Animal Husbandry Resource Centre, The Pearson Product Moment Correlation (PPMC) (r) Naivasha, 9036025 - North Kinangop Forest Station and (Pearson, 1948) was used to establish the strength of 9036241 - Geta Forest Station) was sourced from the relationship between any two variables: NDVI, land cover Kenya Meteorological Service. classes, rainfall and stream flows. The land cover data generated for the years 1973, 1986, RESULTS 2000 and 2013 were then projected using polynomial regression with the data fit achieved using a procedure Rainfall amounts described in Lutus (2013). It was assumed that area under The monthly rainfall amounts (1970-2013) ranged settlement is part of cropland, and so five class projections between 52 mm (2000) and 108 mm (1977) with a mean was applied. The degree of regression was obtained by of 80 mm. The trend in monthly rainfall is provided in setting the number of data pairs minus one.
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