References

HESS

Hydrology and Earth System Sciences

HESS

Hydrol. Earth Syst. Sci.

1607-7938

Copernicus Publications

Göttingen, Germany

10.5194/hess-15-2245-2011

Land use and climate change impacts on the hydrology of the upper Mara River Basin, Kenya: results of a modeling study to support better resource management

Mango

L. M.

¹ Melesse

A. M.

¹ McClain

M. E.

¹ ² Gann

³ Setegn

S. G.

Department of Earth and Environment, Florida International University, Miami, FL 33199, USA

Department of Water Science and Engineering, UNESCO-IHE Institute for Water Education, Delft, The Netherlands

Geographic Information Systems-Remote Sensing Center, Florida International University, Miami, FL 33199, USA

18 07 2011

15 7 2245 2258

2011

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/

This article is available from https://hess.copernicus.org/articles/15/2245/2011/hess-15-2245-2011.html

The full text article is available as a PDF file from https://hess.copernicus.org/articles/15/2245/2011/hess-15-2245-2011.pdf

Some of the most valued natural and cultural landscapes on Earth lie in river basins that are poorly gauged and have incomplete historical climate and runoff records. The Mara River Basin of East Africa is such a basin. It hosts the internationally renowned Mara-Serengeti landscape as well as a rich mixture of indigenous cultures. The Mara River is the sole source of surface water to the landscape during the dry season and periods of drought. During recent years, the flow of the Mara River has become increasingly erratic, especially in the upper reaches, and resource managers are hampered by a lack of understanding of the relative influence of different sources of flow alteration. Uncertainties about the impacts of future climate change compound the challenges. We applied the Soil Water Assessment Tool (SWAT) to investigate the response of the headwater hydrology of the Mara River to scenarios of continued land use change and projected climate change. Under the data-scarce conditions of the basin, model performance was improved using satellite-based estimated rainfall data, which may also improve the usefulness of runoff models in other parts of East Africa. The results of the analysis indicate that any further conversion of forests to agriculture and grassland in the basin headwaters is likely to reduce dry season flows and increase peak flows, leading to greater water scarcity at critical times of the year and exacerbating erosion on hillslopes. Most climate change projections for the region call for modest and seasonally variable increases in precipitation (5–10 %) accompanied by increases in temperature (2.5–3.5 °C). Simulated runoff responses to climate change scenarios were non-linear and suggest the basin is highly vulnerable under low (−3 %) and high (+25 %) extremes of projected precipitation changes, but under median projections (+7 %) there is little impact on annual water yields or mean discharge. Modest increases in precipitation are partitioned largely to increased evapotranspiration. Overall, model results support the existing efforts of Mara water resource managers to protect headwater forests and indicate that additional emphasis should be placed on improving land management practices that enhance infiltration and aquifer recharge as part of a wider program of climate change adaptation.

References 1

Abbaspour, K. C., Johnson, C. A., and van Genuchten, M. T.: Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure, Vadose Zone J., 3(4), 1340–1352, 2004.

Abbaspour, K. C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J., and Srinivasan, R.: Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT, J. Hydrol., 333, 413–430, 2007.

Anderson, R. J., Hardy, E. E., Roach, J. T., and Witmer, R. E.: A Land Use and Land Cover Classification System for use with remote sensor data, United States Geological Survey, United States Government Printing Office, Washington, DC, 1976.

Anyah, R. O. and Qiu, W.: Characteristic 20th and 21st century precipitation and temperature patterns and changes over the Greater Horn of Africa, Int. J. Climatol., 31, <a href="http://dx.doi.org/10.1002/joc.2270">https://doi.org/10.1002/joc.2270</a>, in press, 2011.

Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large area hydrologic modeling modeling and assessment, Part I: Model development, J. Am. Water Resour. Assoc., 34, 73–89, 1998.

Christensen, J. H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R. K., Kwon, W. T., Laprise, R., Magaña Rueda, V., Mearns, L., Menéndez, C. G., Räisänen, J., Rinke, A., Sarr, A., and Whetton, P.: Regional Climate Projections, in: Climate Change: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, UK and New York, NY, USA, 2007.

Di Luzio, M., Srinivasan, R., and Arnold, J. G.: Integration of Watershed Tools and the SWAT Model into BASINS, J. Am. Water Resour. Assoc., 38(4), 1127–1141, 2002.

Duan, Q. Y., Sorooshian, S., and Gupta V.: Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28(4), 1015–1031, 1992.

FAO – Food and Agriculture Organization of the United Nations: Global Forest Resources Assessment Main report, FAO Forestry Paper 163, Food and Agriculture Organization of the United Nations, Rome, 2010.

Gereta, E., Wolanski, E., Borner, M., and Serneels, S.: Use of an ecohydrology model to predict the impact on the Serengeti ecosystem of deforestation, irrigation and the proposed Amala Weir water Diversion Project in Kenya, Ecohydrol. Hydrobiol., 2(1–4), 135–142, 2002.

GoK – Government of Kenya: Report of the Prime Minister's Task Force on the Conservation of the Mau Forests Complex, available online: <a href="http://www.kws.go.ke/export/sites/kws/info/maurestoration/maupublications/Mau_Forest_Complex_Report.pdf">http://www.kws.go.ke/export/sites/kws/info/maurestoration/maupublications/Mau_Forest_Complex_Report.pdf</a>, last access: 15 June 2009.

Hothorn, T., Hornik, K., and Zeileis, A.: Unbiased Recursive Partitioning: A Conditional Inference Framework, J. Comput. Graph. Stat., 15(3), 651–674, 2006.

Hulme, M., Doherty, R. M., Ngara, T., New, M. G., and Lister, D.: African climate change: 1900–2100, Clim. Res., 17, 145–168, 2001.

IPCC – Intergovernmental Panel on Climate Change: Climate Change 2007, Synthesis Report, Cambridge Press, Cambridge, 2007.

IPCC – Intergovernmental Panel on Climate Change: The Science of Climate Change, Cambridge University Press, Cambridge, 2001.

Jacobs, J. H., Angere, J., Vitale, J., Srinivasan, R., and Kaitho, R.: Mitigating Economic Damage in Kenya's Upper Tana River Basin: An Application of Arc-View SWAT, J. Spat. Hydrol., 7(1), 23–46, 2007.

Jayakrishnan, R., Srinivasan, R., Santhi, C., and Arnold, J.: Advances in the application of the SWAT model for water resources management, Hydrol. Process., 19, 749–762, 2005.

KWRMA – Kenya Water Resource Management Authority: Catchment Management Strategy Lake Victoria South Catchment Area, Water Resource Management Authority, Nairobi, Kenya, 2008.

Legesse, D., Vallet-Coulomba, C., and Gasse, F.: Hydrological response of a catchment to climate and land use changes in Tropical Africa: case study South Central Ethiopia, J. Hydrol., 275, 67–85, 2003.

Li, L., Hong, Y., Wang, J., Adler, R. F., Policelli, F. S., Habib, S., Irwn, D., Korme, T., and Okello, L.: Evaluation of the real-time TRMM-based multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, Lake Victoria, Africa, Nat. Hazards, 50(1), 109–123, 2009.

Mati, B. M., Mutie, S., Home, P., Mtalo, F., and Gadain, H.: Land Use Changes in the Transboundary Mara Basin: A Threat to Pristine Wildlife Sanctuaries in East Africa, A Paper presentation at the: 8th International River Symposium, Brisbane, Australia, 6–9 September, 2005.

Mati, B. M., Mutie, S., Gadain, H., Home, P., and Mtalo, F.: Impacts of land-use/cover changes on the hydrology of the transboundary Mara River, Kenya/Tanzania, Lake. Reserv. Manage., 13, 169–177, <a href="http://dx.doi.org/10.1111/j.1440-1770.2008.00367.x">https://doi.org/10.1111/j.1440-1770.2008.00367.x</a>, 2008.

Melesse, A. M., McClain, M., Abira, M., and Mutayoba, W.: Hydrometeorological Analysis of the Mara River Basin, Kenya/Tanzania, Proceedings of the World Environmental and Water Resources Congress 2008, ASCE Conf. Proc. <a href="http://dx.doi.org/10.1061/40976(316)589">https://doi.org/10.1061/40976(316)589</a>, 2008.

Mulungu, D. M. M. and Munishi, S. E.: Simiyu River catchment parameterization using SWAT model, Phys. Chem. Earth, 32, 1032–1039, 2007.

Mutie, S., Mati, B., Gadain, H., and Gathenya, J.: Evaluating land use change effects on river flow using USGS geospatial stream flow model in Mara River basin, Kenya, A Paper presentation at the 2nd Workshop of the EARSeL SIG on Land Use and Land Cover, Bonn, 28–30 September, 2006.

Neitsch, S. L., Arnold, J. G., Kiniry J. R, and Williams, J. R.: Soil and Water Assessment Tool, Theoretical Documentation: Version, Agricultural Research Service and Texas A & M Blackland Research Center, Temple, TX, USDA, 2005.

Olang, L. O. and Fürst, J.: Effects of land cover change on flood peak discharges and runoff volumes: model estimates for the Nyando River Basin, Kenya, Hydrol. Process., 25, 80–89, 2011.

Ruosteenoja, K., Carter, T. R., Jylhä, K., and Tuomenvirta, H.: Future climate in world regions: an intercomparison of model-based projections for the new IPCC emissions scenarios, The Finnish Environment vol. 644, Finnish Environment Institute, 83 pp., 2003.

Schuol, J. and Abbaspour, K. C.: Using monthly weather statistics to generate daily data in a SWAT model application to west Africa, Ecol. Model., 201(3–4), 301–311, 2007.

Setegn, S. G., Srinivasan, R., Melesse, A. M., and Dargahi, B.: SWAT model application and prediction uncertainty analysis in the Lake Tana Basin, Ethiopia, Hydrol. Process., 23(26), 3738–3750, 2009.

Setegn, S. G., Rayner, D., Melesse, A. M., Dargahi, B., and Srinivasan, R.: Impact of climate change on the hydroclimatology of Lake Tana Basin, Ethiopia, Water Resour. Res., 47, W04511, <a href="http://dx.doi.org/10.1029/2010WR009248">https://doi.org/10.1029/2010WR009248</a>, 2011.

Shongwe, M. E., van Oldenborgh, G. J., van den Hurk, B. J. J. M., and van Aalst, M. A.: Projected changes in extreme precipitation in Africa under global warming, Part II: East Africa, J. Climate, in press, <a href="http://dx.doi.org/10.1175/2010JCLI2883.1">https://doi.org/10.1175/2010JCLI2883.1</a>, 2011.

Shrestha, M. S., Artan, G. A., Bajracharya, S. R., and Sharma, R. R.: Using satellite-based rainfall estimates for streamflow modeling: Bagmati Basin, J. Flood Risk Manage., 1, 89–99, 2008.

Van Griensven, A. and Meixner, T.: Methods to quantify and identify the sources of uncertainty for river basin water quality models, Water Sci. Technol., 53(1), 51–59, 2006.

Vorosmarty, C. J., Green, P., Salisbury, J., and Lammers, R. B.: Global water resources: Vulnerability from climate change and population growth, Science, 289, 284–288, 2000.

Wilk, J., Kniveton, D., Andersson, L., Layberry, R., Todd, M. C., Hughes, D., Ringrose, S., and Vanderpost, C.: Estimating rainfall and water balance over the Okavango River Basin for hydrological applications, J. Hydrol., 331, 18–29, 2006.

WRI – World Resources Institute, Department of Resource Surveys and remote Sensing, Ministry of Environment and Natural resources, Kenya, Central Bureau of Statistics, Ministry of Planning and Development, Kenya; and International Livestock Research Institute: Nature's Benefits in Kenya: An Atlas of Ecosystems and Human Well-Being, World Resources Institute, Washington, DC, and Nairobi, 2007.

Xie, P. and Arkin, P. A.: Global precipitation: A 17-Year monthly analysis based on gauge observations, Satellite Estimates, and numerical model outputs, B. Am. Meteorol. Soc., 78, 112539–112558, 1997.