Advertisement

Regional Environmental Change

, Volume 15, Issue 7, pp 1269–1280 | Cite as

The effects of climate and changing land use on the discharge regime of a small catchment in Tanzania

  • Marco Natkhin
  • Ottfried Dietrich
  • Meike Pendo Schäfer
  • Gunnar Lischeid
Original Article

Abstract

Increasing pressure on water resources makes it necessary to understand the reasons for the changes in the run-off characteristic of the Ngerengere River in Tanzania during recent years. Changing land use and changes in climate boundaries are identified as effects. A combination of statistical analysis and the use of the hydrological model SWAT were chosen to handle the problem of poor data quantity and quality with non-overlapping periods. Changes in the discharge regime were identified with the 5th percentile of the flow duration curve as an indicator for high-flow events, with an indicator for low-flow duration and with the base flow index. The analysis showed that climate boundaries and changing land use do not have a uniform effect on discharge in the catchment. Changing land use affects surface run-off and increases floods in the mountainous areas. Changes in climate boundaries increase the duration of low flow and no flow in the Ngerengere catchment. Changes in climate conditions and land use had antipodal effects on parts of the discharge regime. Thus, the observed changes in land use and climate conditions partially compensate for each other.

Keywords

Ngerengere catchment SWAT Flow duration curve Base flow index 

Notes

Acknowledgments

The work reported here was undertaken as part of the project ”Resilient Agro-landscapes to Climate Change in Tanzania (ReACCT)” funded by the Federal Ministry for Economic Cooperation and Development (BMZ) and Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) under a Research Program: “Adaptation of African Agriculture to Climate Change”. The authors would like to thank the WRBWO, TMA, Sokoine University of Agriculture and University of Dar es Salam for their support.

References

  1. Arnold JG, Fohrer N (2005) SWAT2000: current capabilities and research opportunities in applied watershed modelling. Hydrol Process 19(3):563–572CrossRefGoogle Scholar
  2. Arnold JG, Allen PM, Muttiah R, Bernhardt G (1995) Automated base–flow separation and recession analysis techniques. Ground Water 33(6):1010–1018CrossRefGoogle Scholar
  3. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment—part 1: model development. J Am Water Resour Assoc 34(1):73–89CrossRefGoogle Scholar
  4. Batjes NH (2004) SOTER–based soil parameter estimates for Southern Africa. ISRIC—World Soil Information 2004/04, WageningenGoogle Scholar
  5. Bronaugh D, Werner A (2009) ‘Zyp’ package—Zhang + Yue–Pilon trends package. http://www.r-project.org
  6. Bruijnzeel LA (1990) Hydrology of moist tropical forests and effects of conversion: a state of knowledge review. UNESCO. IHP humid tropics programme series 2. ParisGoogle Scholar
  7. Calder IR (2002) Forests and hydrological services: reconciling public and science perceptions. Land Use Water Resour Res 2(2):1–12Google Scholar
  8. FAO (2004) Map of reference evapotranspiration. FAO. http://www.fao.org/climatechange/54638/en/
  9. Githui FW (2008) Assessing the impacts of environmental change on the hydrology of the Nzoia catchment, in the Lake Victoria Basin Department of Hydrology and Hydraulic. Engineering Faculty of Engineering, Vrije Universiteit Brussel Brussels, BrusselsGoogle Scholar
  10. Gomani MC, Dietrich O, Lischeid G, Mahoo H, Mahay F, Mbilinyi B, Sarmett J (2010) Establishment of a hydrological monitoring network in a tropical African catchment: an integrated participatory approach. Phys Chem Earth 35(13–14):648–656CrossRefGoogle Scholar
  11. Gross J (2009) ‘Nortest’ package—tests for normality. http://cran.r-project.org/web/packages/nortest/index.html
  12. Holländer HM, Blume T, Bormann H, Buytaert W, Chirico GB, Exbrayat JF, Gustafsson D, Hölzel H, Kraft P, Stamm C, Stoll S, Blöschl G, Flühler H (2009) Comparative predictions of discharge from an artificial catchment (Chicken Creek) using sparse data. Hydrol Earth Syst Sci 13(11):2069–2094CrossRefGoogle Scholar
  13. Jarvis A, Reuter HI, Nelson A, Guevara E (2006) Hole–filled seamless SRTM data V3. International Centre for Tropical Agriculture (CIAT), Colombia, USAGoogle Scholar
  14. Kashaigili J (2008) Impacts of land–use and land–cover changes on flow regimes of the Usangu wetland and the Great Ruaha River, Tanzania. Phys Chem Earth 33(8–13):640–647CrossRefGoogle Scholar
  15. McLeod AI (2011) ‘Kendall’ package—Kendall rank correlation and Mann–Kendall trend test. http://www.stats.uwo.ca/faculty/aim
  16. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans Asabe 50(3):885–900CrossRefGoogle Scholar
  17. MPEE (2007) Morogoro region—socio–economic profile. Ministry of Planning, Economy and Empowerment, Dar Es SalaamGoogle Scholar
  18. Mtalo F, Mulungu D, Mwanuzi F, Mkhandi S, Kimaro T, Valimba P (2005) Hydrological analysis for the Eastern ArcMountain forests. Conservation and Management of the Eastern Arc Mountain Forests—Forestry and Beekeeping Division, Dar er SalamGoogle Scholar
  19. Mulungu DMM, Munishi SE (2007) Simiyu River catchment parameterization using SWAT model. Phys Chem Earth 32(15–18):1032–1039CrossRefGoogle Scholar
  20. Mwakalila S (2005) Water resource use in the Great Ruaha Basin of Tanzania. Phys Chem Earth 30(11–16):903–912CrossRefGoogle Scholar
  21. Mwakalila S (2011) Vulnerability of people’s livelihoods to water resources availability in semi arid areas of Tanzania. J Water Resour Prot 3:678–685CrossRefGoogle Scholar
  22. Mwamila TB, Kimwaga RJ, Mtalo FW (2008) Eco–hydrology of the Pangani River downstream of Nyumba ya Mungu reservoir, Tanzania. Phys Chem Earth 33(8–13):695–700CrossRefGoogle Scholar
  23. Natkhin M, Steidl J, Dietrich O, Dannowski R, Lischeid G (2012) Differentiating between climate effects and forest growth dynamics effects on decreasing groundwater recharge in a lowland region in Northeast Germany. J Hydrol 448–449:245–254CrossRefGoogle Scholar
  24. Ndomba PM, Mtalo FW, Killingtveit A (2005) The suitability of SWAT model in sediment yield modeling for ungauged catchments: a case of Simiyu River subcatchment, Tanzania. In: 3rd International SWAT Conference 61–69, ZürichGoogle Scholar
  25. Ndomba P, Mtalo F, Killingtveit A (2008) SWAT model application in a data scarce tropical complex catchment in Tanzania. Phys Chem Earth 33(8–13):626–632CrossRefGoogle Scholar
  26. Neitsch SL, Arnold JG, Kiniry JR, Srinivasan R, Williams JR (2009) Soil and water assessment tool—input/output file documentation Grassland, Texas Water Resources Institute Technical Report No. 365, Texas A&M University, TexasGoogle Scholar
  27. Nyenzi BS, Kiangi PMR, Rao NNP (1981) Evaporation values in East-Africa. Arch Meteorol Geophys Bioclimatol Ser B Theor Appl Climatol 29(1–2):37–55CrossRefGoogle Scholar
  28. Palamuleni GL, Ndomba PM, Annegarn HJ (2011) Evaluating land cover change and its impact on hydrological regime in Upper Shire river catchment, Malawi. Reg Environ Change 11(4):845–855CrossRefGoogle Scholar
  29. R Development Core Team (2006) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  30. Sarmett J, Anderson E, Mandari A (2008) Wami River Sub–Basin, Tanzania—initial environmental flow assessment. Wami–Ruvu Basin Water Office, TanzaniaGoogle Scholar
  31. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389CrossRefGoogle Scholar
  32. Smakhtin VU (2001) Low flow hydrology: a review. J Hydrol 240(3–4):147–186CrossRefGoogle Scholar
  33. Thornton PK, Jones PG, Owiyo T et al (2006) Mapping climate vulnerability and poverty in Africa. Report to the Department for International Development. Department for International Development, ILRI, NairobiGoogle Scholar
  34. Valimba P (2008) Temporal flow variations: a challenge for water management in Tanzania. In: GLOBAL CHANGES and WATER RESOURCES: confronting the expanding and diversifying pressures. IWRA, Montpellier, France. http://www.iwra.org/congress/2008
  35. Vogel RM, Fennessey NM (1994) Flow–duration curves. I: new interpretation and confidence intervals. J Water Resour Plan Manag 120((4):485–504CrossRefGoogle Scholar
  36. Yanda PZ, Munishi PKT (2007) Hydrologic and land use/cover change analysis for the Ruvu River (Uluguru) and Sigi River (East Usambara) Watersheds. Dar es Salaam. Eastern Arc Mountains Conservation Endowment Fund (EAMCEF), Morogoro, Tanzania. http://easternarc.or.tz/downloads/Uluguru/Final%20Report%20Revised_20_04_2007.pdf
  37. Yapo PO, Gupta HV, Sorooshian S (1996) Automatic calibration of conceptual rainfall–runoff models: sensitivity to calibration data. J Hydrol 181(1–4):23–48CrossRefGoogle Scholar
  38. Yue S, Pilon P, Phinney B, Cavadias G (2002) The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Process 16(9):1807–1829CrossRefGoogle Scholar
  39. Zambrano–Bigiarini M (2011) ‘hydroTSM’ package—time series management, analysis and interpolation for hydrological modelling. http://cran.r-project.org/web/packages/hydroTSM/
  40. Zhao G, Hörmann G, Fohrer N, Zhang Z, Zhai J (2010) Streamflow trends and climate variability impacts in Poyang Lake Basin, China. Water Resour Manag 24:689–706CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marco Natkhin
    • 1
    • 2
  • Ottfried Dietrich
    • 1
  • Meike Pendo Schäfer
    • 1
  • Gunnar Lischeid
    • 1
  1. 1.Leibniz Centre for Agricultural Landscape ResearchMünchebergGermany
  2. 2.Thünen-Institute for Forest EcosystemsEberswaldeGermany

Personalised recommendations