Assessment of water resources availability and demand in the Mara River Basin

Dessu, Shimelis Behailu; Melesse, Assefa M.; Bhat, Mahadev G.; McClain, Michael E.

doi:10.1016/j.catena.2013.11.017

Cited by 85 publications

(50 citation statements)

References 19 publications

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“…The first two tributaries are perennial while the remaining tributaries are ephemeral, which generally dry out during dry periods. In total, the 70 river is 395 km long (Dessu et al, 2014) and its catchment covers an area of about 11,500 km 2 (McClain et al, 2013) of which 65% is located in Kenya (Mati et al, 2008).…”

Section: Site Description Of the Mara River Basin And Data Availabilitymentioning

confidence: 99%

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Modelling the Mara River Basin with data uncertainty using water levels for calibration

Hulsman

Bogaard

Savenije

2017

Preprint

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Abstract. Hydrological models play an important role in Water Resources Management. In hydrological modelling, discharge data is generally required for calibration. To obtain continuous time series, water levels are usually converted into discharge by using a rating curve. However with this methodology, uncertainties are introduced in the discharge data due to insufficient observations, inadequate rating curve fitting procedures, 10 extrapolation or temporal changes in the river geometry. Unfortunately, this is often the case in many African river basins. In this study, a semi-distributed rainfall runoff model has been applied to the Mara River Basin for the assessment of the water availability. To reduce the effect of discharge uncertainties in this model, water levels instead of discharge time series were used for calibration. In this model, seven sub-catchments are distinguished and four hydrological response units: forest, shrubs, cropland and grassland. To calibrate the 15 model on water level data, modelled discharges have been converted into water levels using cross-section observations and the Strickler formula. In addition, new geometric rating curves have been obtained based on modelled discharge, observed water level and the Strickler formula. This procedure resulted in good and consistent model results during calibration and validation. The hydrological model was able to reproduce the water depths for the entire basin as well as for the Nyangores sub-catchment in the north. The geometric and 20 recorded (i.e. existing) rating curves were significantly different at Mines, the catchment outlet, probably due to uncertainties in the recorded discharge time series. At Nyangores however, the geometric and recorded discharge were almost identical. In addition, it has been found that the precipitation estimation methodology influenced the model results significantly. Application of a single station for each sub-catchment resulted in flashier responses whereas Thiessen averaged precipitation resulted in more dampened responses. In conclusion, 25 by using water level time series for calibrating the hydrological model of the Mara River Basin promising model results were obtained. For this river basin, the main limitation for obtaining an accurate hydrograph representation was the inadequate knowledge on the spatial distribution of the precipitation.Hydrol. Earth Syst. Sci. Discuss., https://doi

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Section: Site Description Of the Mara River Basin And Data Availabilitymentioning

confidence: 99%

“…The largest annual rainfall can be found in the upstream area of the catchment: between 1000 and 1750 mm/yr. In the middle and downstream areas, the annual rainfall is between 900 and 1000 mm/yr and between 300 and 850 mm/yr, respectively (Dessu et al, 2014).…”

Section: Site Description Of the Mara River Basin And Data Availabilitymentioning

confidence: 99%

Modelling the Mara River Basin with data uncertainty using water levels for calibration

Hulsman

Bogaard

Savenije

2017

Preprint

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“…The Mara River and its tributaries are an essential source of water for domestic needs, agriculture, pastoralism and wildlife, including tourism, in Kenya and Tanzania (Mati et al, 2008;. Although extensive research has been undertaken into the management of the terrestrial ecosystems of the Serengeti National Park and Maasai Mara National Reserve, there are limited studies on the effects of land use threats to the wellbeing of the Mara River, with only site-specific evaluations of the environmental flows of the river (Broten and Said, 1995; Gereta et al, 2002;Onjala, 2002;Karanja, 2003;Lamprey and Reid, 2004;Hoffman, 2007;Mati et al, 2008;Atisa, 2009;GLOWS-FIU, 2012;Majule, 2010;Hoffman et al, 2011;Ogutu et al, 2011;Kiambi et al, 2012;Dessu et al, 2014).…”

Section: Study Areamentioning

confidence: 99%

“…In the Mara River case study, a review of land use and land cover (Mati et al, 2008;Atisa et al, 2014), hydrology (Mango et al, 2011;McClain et al, 2014), the vision for the case study, current and future land and water resource use options and socio-ecological importance (Karanja, 2002;LVBC and WWF-ESARPO, 2010;Mango et al, 2011;GLOWS-FIU, 2012;Dessu et al, 2014;Dutton et al, 2013) was used to select risk regions. Ten risk regions were selected for the Mara River case study which conformed to catchment boundaries, ecoregions, land use practices and the international boundary (Fig.…”

Section: Step 3: Risk Region Selectionmentioning

confidence: 99%

A regional-scale ecological risk framework for environmental flow evaluations

O’Brien

Dickens

Hines

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Environmental flow (E-flow) frameworks advocate holistic, regional-scale, probabilistic E-flow assessments that consider flow and non-flow drivers of change in a socioecological context as best practice. Regional-scale ecological risk assessments of multiple stressors to social and ecological endpoints, which address ecosystem dynamism, have been undertaken internationally at different spatial scales using the relative-risk model since the mid-1990s. With the recent incorporation of Bayesian belief networks into the relativerisk model, a robust regional-scale ecological risk assessment approach is available that can contribute to achieving the best practice recommendations of E-flow frameworks. PROBFLO is a holistic E-flow assessment method that incorporates the relative-risk model and Bayesian belief networks (BN-RRM) into a transparent probabilistic modelling tool that addresses uncertainty explicitly. PROBFLO has been developed to evaluate the socio-ecological consequences of historical, current and future water resource use scenarios and generate E-flow requirements on regional spatial scales. The approach has been implemented in two regional-scale case studies in Africa where its flexibility and functionality has been demonstrated. In both case studies the evidence-based outcomes facilitated informed environmental management decision making, with trade-off considerations in the context of social and ecological aspirations. This paper presents the PROBFLO approach as applied to the Senqu River catchment in Lesotho and further developments and application in the Mara River catchment in Kenya and Tanzania. The 10 BN-RRM procedural steps incorporated in PROBFLO are demonstrated with examples from both case studies. PROBFLO can contribute to the adaptive management of water resources and contribute to the allocation of resources for sustainable use of resources and address protection requirements.

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“…The mean annual rainfall varies from 1,000 mm to 1,750 mm, 900 mm to 1,000 mm and 300 mm to 800 mm in the upper, middle and lower parts of the basin respectively (Dessu et al, 2014;Dessu & Mellesse, 2012). …”

Section: In-situ Measurementsmentioning

confidence: 99%

Runoff modeling of the Mara River using satellite observed soil moisture and rainfall

Mwania¹,

Velde²,

Vekerdy³

et al. 2017

RWJour

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Hydrological models are essential in water resources management. However modeling in poorly gauged catchments is a big challenge. Recent studies have shown that satellite based hydrological and meteorological data has the potential of being part of the solution towards overcoming this challenge. In this study, we modify the conceptual lumped rainfall-runoff model by Meier et al. (2011) season runoff, but needs further improvement to be able to reliably simulate wet season runoff. Nevertheless, this study demonstrates the potential role operational satellite based soil moisture and rainfall products can play in quantifying the available water resources particularly in the many un-gauged river basins across Africa.

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Assessment of water resources availability and demand in the Mara River Basin

Cited by 85 publications

References 19 publications

Modelling the Mara River Basin with data uncertainty using water levels for calibration

Modelling the Mara River Basin with data uncertainty using water levels for calibration

A regional-scale ecological risk framework for environmental flow evaluations

Runoff modeling of the Mara River using satellite observed soil moisture and rainfall

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