The Impact of Resource Utilization on the Hydrology of the Mau Hills Forest in Kenya

Krhoda, George Okoye

doi:10.2307/3673447

Cited by 22 publications

(20 citation statements)

References 3 publications

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“…surface runoff, baseflow and evapotranspiration were also assessed based on the knowledge of the watershed as well as published literature in the region (e.g. Dagg and Blackie, , ; Krhoda, ; Mati et al ., ; Water Resources and Energy Management, ; Mutiga et al ., ; Dessu and Melesse, ; Recha et al ., ; Baker and Miller, ; Mwangi et al ., ). Dagg and Blackie (, ) reported that ‘deep percolation loss’ was minimal for their experimental study site in Mau forest.…”

Section: Methodsmentioning

confidence: 99%

Modelling the impact of agroforestry on hydrology of Mara River Basin in East Africa

Mwangi

Jülich

Patil

et al. 2016

Hydrological Processes

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Land‐use change is one of the main drivers of watershed hydrology change. The effect of forestry related land‐use changes (e.g. afforestation, deforestation, agroforestry) on water fluxes depends on climate, watershed characteristics and spatial scale. The Soil and Water Assessment Tool (SWAT) model was calibrated, validated and used to simulate the impact of agroforestry on the water balance in the Mara River Basin (MRB) in East Africa. Model performance was assessed by Nash–Sutcliffe Efficiency (NSE) and Kling–Gupta Efficiency (KGE). The NSE (and KGE) values for calibration and validation were: 0.77 (0.88) and 0.74 (0.85) for the Nyangores sub‐watershed, and 0.78 (0.89) and 0.79 (0.63) for the entire MRB. It was found that agroforestry in the watershed would generally reduce surface runoff, mainly because of enhanced infiltration. However, it would also increase evapotranspiration and consequently reduce baseflow and overall water yield, which was attributed to increased water use by trees. Spatial scale was found to have a significant effect on water balance; the impact of agroforestry was higher at the smaller headwater catchment (Nyangores) than for the larger watershed (entire MRB). However, the rate of change in water yield with an increase in area under agroforestry was different for the two and could be attributed to the spatial variability of climate within the MRB. Our results suggest that direct extrapolation of the findings from a small sub‐catchment to a larger watershed may not always be accurate. These findings could guide watershed managers on the level of trade‐offs that might occur between reduced water yields and other benefits (e.g. soil erosion control, improved soil productivity) offered by agroforestry. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Methodsmentioning

confidence: 99%

Modelling the impact of agroforestry on hydrology of Mara River Basin in East Africa

Mwangi

Jülich

Patil

et al. 2016

Hydrological Processes

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“…Two headwater tributaries of the Mara, the Nyangores and Amala Rivers, descend from the Mau escarpment, one of Kenya's five so‐called water towers. The Mau Forest, which crowns the escarpment, has been subject to an alarming rate of deforestation in recent years (Baldyga et al ., ) that appears to have affected the hydrology of a number of basins (Krhoda ; Raini, ; Olang et al ., ), including possibly the Mara. Historic hydrometeorological conditions in the Nyangores and Amala basins have been studied (Melesse et al ., ), but without consideration of temporal change and variability.…”

Section: Introductionmentioning

confidence: 99%

Rating curve uncertainty and change detection in discharge time series: case study with 44‐year historic data from the Nyangores River, Kenya

Juston

Jansson

Gustafsson

2013

Hydrological Processes

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The intersection of the developing topic of rating curve and discharge series uncertainty with the topic of hydrological change detection (e.g., in response to land cover or climatic change) has not yet been well studied. The work herein explores this intersection, with consideration of a long‐term discharge response (1964–2007) for a ~650‐km2 headwater basin of the Mara River in west Kenya, starting with stream rating and daily gauge height data. A rating model was calibrated using Bayesian methods to quantify uncertainty intervals in model parameters and predictions. There was an unknown balance of random and systemic error in rating data scatter (a scenario not likely unique to this basin), which led to an unknown balance of noise and information in the calibrated statistical error model. This had implications on testing for hydrological change. Overall, indications were that shifts in basin's discharge response were rather subtle over the 44‐year period. A null hypothesis for change using flow duration curves (FDCs) from four different 8‐year data intervals could be either accepted or rejected over much of the net flow domain depending on different applications of the statistical error model (each with precedence in the literature). The only unambiguous indication of change in FDC comparisons appeared to be a reduction in lowest baseflow in recent years (flows with >98% exceedance probability). We defined a subjective uncertainty interval based on an intermediate balance of random and systematic error in the rating model that suggested a possibility of more prevalent impacts. These results have relevance to management in the Mara basin and to future studies that might establish linkages to historic land use and climatic factors. The concern about uncertain uncertainty intervals (uncertainty2) extends beyond the Mara and is relevant to testing change where non‐random rating errors may be important and subtle responses are investigated. Copyright © 2013 John Wiley & Sons, Ltd.

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“…The soils are deep and well-drained, classified as humic Nitisols (ISRIC, 2007;Krhoda, 1988). The area has a bi-modal rainfall pattern with highest rainfall between April and July (long rains) and October and December (short rains).…”

Section: Study Areamentioning

confidence: 99%

“…Afromontane mixed forest, with species including Podocarpus milanjianus, Juniperus procera and Olea hochstetteri (Kinyanjui, 2011;Krhoda, 1988). The vegetation changes into bamboo forest (Arundinaria alpina) above 2 300 m elevation.…”

Section: Study Areamentioning

confidence: 99%

Land use alters dominant water sources and flow paths in tropical montane catchments in East Africa

Jacobs¹,

Timbe²,

Weeser³

et al. 2018

Preprint

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Abstract. Conversion of natural forest to other land uses could lead to significant changes in catchment hydrology, but the nature of these changes has been insufficiently investigated in tropical montane catchments, especially in Africa. To address 20 this knowledge gap, we identified stream water sources and flow paths in three tropical montane sub-catchments (27-36 km²) with different land use (natural forest, smallholder agriculture and commercial tea plantations) within a 1 021 km² catchment in the Mau Forest Complex, Kenya. Weekly samples were collected from stream water, precipitation and soil water for 75 weeks and analysed for stable water isotopes (δ 2 H and δ 18 O) for mean transit time estimation, whereas trace element samples from stream water and potential end members were collected over a period of 55 weeks for end member mixing analysis. 25Stream water mean transit time was similar (~4 years) in the three sub-catchments, and ranged from 3.2-3.3 weeks in forest soils and 4.5-7.9 weeks in pasture soils at 15 cm depth to 10.4-10.8 weeks in pasture soils at 50 cm depth. The contribution of springs and wetlands to stream discharge increased from 18, 1 and 48 % during low flow to 22, 51 and 65 % during high flow in the natural forest, smallholder agriculture and tea plantation sub-catchments, respectively. The dominant stream water source in the tea plantation sub-catchment was spring water (56 %), while precipitation was dominant in the smallholder 30 agriculture (59 %) and natural forest (45 %) sub-catchments. These results confirm that catchment hydrology is strongly influenced by land use, which could have serious consequences for water-related ecosystem services, such as provision of clean water.Hydrol. Earth Syst. Sci. Discuss., https://doi

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The Impact of Resource Utilization on the Hydrology of the Mau Hills Forest in Kenya

Cited by 22 publications

References 3 publications

Modelling the impact of agroforestry on hydrology of Mara River Basin in East Africa

Modelling the impact of agroforestry on hydrology of Mara River Basin in East Africa

Rating curve uncertainty and change detection in discharge time series: case study with 44‐year historic data from the Nyangores River, Kenya

Land use alters dominant water sources and flow paths in tropical montane catchments in East Africa

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