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Hydro-climatology Characterization of Degraded Lwamunda Forest Catchment Based on Probability Distributions
Earth Sciences
Volume 9, Issue 2, April 2020, Pages: 65-75
Received: Nov. 21, 2019; Accepted: Jan. 27, 2020; Published: Mar. 17, 2020
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Ausi Abubakar Ssentongo, Department of Geography, Geo-informatics and Meteorology, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, South Africa
Nsubuga Francis Waswa, Department of Geography, Geo-informatics and Meteorology, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, South Africa
Daniel Darkey, Department of Geography, Geo-informatics and Meteorology, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, South Africa
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Hydroclimatology assessment is conventionally based on area data for identification of change patterns and trends. In this paper, monthly averages, maximum seasonal and maximum annual hydro- climatology data series from Lwamunda forest catchment area in central Uganda have been analyzed in order to determine the appropriate probability distribution models for the underlying climatology (i.e. rainfall, soil moisture content, evapotranspiration and temperature). A total of 7 probability distributions were considered and three goodnessof- fit tests were used to evaluate the best-fit probability distribution model for each hydro-climatology data series. They were Lilliefors (D), Anderson-Darling (AD), and Cramer-Von Mises (W2). A ranking metric based on the test statistic from the three GoF tests was used to select the most appropriate probability distribution model capable of reproducing the statistics of the hydroclimatological data series. The best fit probability distribution was selected based on the minimum sum of the three test statistic. Results showed that different best fit probability distribution models were identified for the different data series depending on location and on temporal scales which corroborate with those reported in literature. With the exception of soil moisture content for annual and seasonal maximum series who have the same best fit model. The same applied to evapotranspiration seasonal maximum and near surface temperature seasonal maximum as well as monthly near surface temperatures have the same best fit model. The soil moisture content data series was best fit by the Weibull probability distribution, rainfall series was best fit by Chi square and Gamma probability distributions. The evapotranspiration data series was best fit by Logistic and Extreme value maximum (Gumbel) probability distributions. Finally for near surface temperature it was best fitted by Logistic and Gumbel probability distributions. The contribution of this study lies in the use of hydroclimatological data series including soil moisture content from the area that had forest cover change to analyzeits impact on water resources patterns. The contribution is important for agricultural planning and forest managers’ simulation of forest degradation impacts.
Hydroclimatilogy, Probability Distributions, Climate Variability, Water Resources
To cite this article
Ausi Abubakar Ssentongo, Nsubuga Francis Waswa, Daniel Darkey, Hydro-climatology Characterization of Degraded Lwamunda Forest Catchment Based on Probability Distributions, Earth Sciences. Vol. 9, No. 2, 2020, pp. 65-75. doi: 10.11648/
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Guido, D. S. Saleem, J. and Kaufmann (2002).”Investigating Soil Moisture Feedbacks on Precipitation with tests of Granger causality”. Advances in water resources. Vol. 25, pp 1305-1312.
Shi Zhong, Tao Yong, Jian Zhu and Futing Wu (2019).”A modeling study of the Influence of initial soil moisture on summer precipitation during the East Asian Summer Monsoon”. Dynamics of atmospheres and Oceans. Issue 85, pp. 72-82.
Nicholson, S. E., (2000) “The nature of rainfall variability over Africa on time scale of decades to millennia. Global Planet”. Change, 26, 137–158, doi: 10.1016/S0921-8181(00)00040-0.
Li, X.-Y., P.-J. Shi, Y.-L. Sun, J. Tang, and Z.-P. Yang, (2006) “Influence of various in-situ rainwater harvesting methods on soil moisture and growth of Tamarixramosissima in thesemiarid loess region of China”. For. Ecol. Manage., 233, 143–148, doi: 10.1016/j.foreco.2006.06.013.
Kamruzzaman, M., S. Beecham, and A. V. Metcalfe, (2013) “Climatic influences on rainfall andrunoff variability in the southeast region of the Murray-Darling basin”. Int. J. Climatol., 33, 291–311, doi: 10.1002/joc.3422
Mubiru, D. N, Komutunga, E. Agona A. Apok, A and Ngara, T. (2012). “Characterising agrometeorological climate risks and uncertainities: crop production in Uganda”, South African Journal of Sciences, Vol. 108, No ¾, pp 108-118.
Lazaro, R., F. S. Rodrigo, L. Gutierrez, F. Domingo, and J. Puigdefafregas, (2001) Analysis of a 30-year rainfall record (1967-1997) in semi-arid SE Spain for implications on vegetation.” J. Arid Environ., 48, 373–395, doi: 10.1006/jare.2000.0755.
Jackson, I. J., (1977) “Climate, Water and Agriculture in the Tropics”. Longman, 248 pp.
Kizza Michael, et al, (2009). “Temporal rainfall variability in the Lake Victoria Basin in East Africa during the twentieth Century”Springer- Verlag.
Government of Uganda (GOU), (2015). “Economic Assessment of the Impacts of Climate Change in Uganda, Final Study Report”. Ministry of water and Environment, Climate Change Department, Kampala.
Nsubuga, F. W. N.. Olwoch, J. M. and Rautenbach, C. J. dew and Botai, O. J (2014b). “Analysis of midtwentieth century rainfall trends and variability over southwestern Uganda”. Theoretical and Applied Climatology, Vol. 115, pp 53-57.
National Environment ManagementAuthority (NEMA). (2008). “State of Environment Report for Uganda 2008” National Environment Management Authority, Kampala, p. 282.
Conway D. (2005). “From headwater tributaries to international river: Observing and adapting to climate variability and change in the Nile basin”. Global Environmental Change, Vol. 15 No. 2, pp. 99-114. IJCCSM 10, 5768.
Asadullah, A. Mcintyre, N. and Kigobe, M. (2008). “Evaluation of five satellite products for estimation of rainfall over Uganda”. Hydrological Sciences Journal, Vol. 53 No. 6, pp. 1137-1150.
Nsubuga, F. W. N. (2018). “Climate change and variability: A review of what is known and Ought to be known for Uganda”. International Journal of Climate Change Strategies and management. Vol. 10, No. 3. pp 752-771.
Nsubuga, F. W. N., Botai O. J., Olwoch, J. M. and Rautenbach, C. J. dew. Bevis, Y and Adetunji, A. O. (2014c). “The nature of rainfall in the main drainage sub-basins of Uganda”. Hydrological Sciences Journal, Vol. 59 No. 2, pp 278-299.
FEWSNET (2012). “Famine Early Warning Systems Network. A Climate Trend Analysis of Uganda” available at: ttp:// (accessed 9 January 2017 at 22: 45 CET).
Nyanja, Pm. And Batelaan, O. (2009).”Estimating the effects of climate change on groundwater recharge and base flow in the upper Ssezibwa catchment, Uganda”. Hydrological Sciences Journal. Vol 54. No. 4. Pp 713-726.
Jassogne, L. Laderach, P. and Van Asten, P. (2013) “The impact of climate change on coffee in Uganda. Lessons from a case study in the Rwenzori Mountains”. Oxfam Research Reports, IITA. CIAT, Oxfam, ISBN 978-1-78077-262-2. (accessed 10 January 2017 at 10: 25 CET).
Badjeck, M. C. Allison, E. H. Halls, A. S and Dulvy, N. K. (2010). “Impacts of climate variability and change on fishery- based livelihoods”. Marine Policy, Vol. 34 No. 3, pp 375-383.
Kilimani, N. (2013). “Water resources accounts for Uganda: Use and policy relevancy”. ERSA working paper No. 365.
Nsubuga, F. W. N.. Olwoch, J. M. and Rautenbach, C. J. dew. (2014a) “Variability properties of daily and monthly observed near- surface temperatures in Uganda: 1960-2008”. International Journal of Climatology, Vol. 34, pp. 303-314.
Mingliang L. Hanquin T, Guangsheng C, Wei R, Zhang C and Jiyuan L. (2008)“Effects of land –use and land cover change on evapotransipiration and water yield in China during 1900-2000”. Journal of the American Water Resources Association. Vol. 44. No. 5.
Marcos H. C, Michael T. C, Guyot J L. (2009). “Effects of Climate variability and Deforestation on Surface water regimes. Amazonia and Global change, Geophysical Monograph series 186.
Gautam, M. (2006).”Managing drought in sub-Saharan Africa: policy perspectives”. Invited paper prepared for a Panel session on drought: Economic consequences and policies for mitigation at the IAAF conference, Gold cast, Queensland, Australia, August 12-18, 2006
Shiferaw, B. Tesfaye K. Kassie, M. Abate, T. Prasanna, B. M, and Menkir, A. (2014) “Managing Vulnerability to drought and enhancing livelihood resiliencein sub-Saharan Africa: Technological, Institutional and Policy options”. Weather and Climate Extremes, 3, 67-79.
Ssentongo A. A, Darkey. D, and J. Mutyaba. (2018). Detecting forest cover and ecosystem service change using integrated approach of remotely sensed derived indices inthe central districts of Uganda. South AfricanJournal of Geomatics, Vol. 7, No. 1, pp 46-63.
Basalirwa, C. P. K (1995). “Delineation of Uganda into climatological rainfall zones using the method of principal component analysis”, International Journal of Climatology, Vol. 15, No 10, pp. 1161-1177.
Muchuru S Botai, C. M, Botai J. O. and Adeola, A. M.(2015) “ The hydrometeorology of the Kariba Catchment Area Based on the Probability Distributions” Earth interactions. Vol. 19, paper No. 14.
Wan, H., X. Zhang, and E. M. Barrow. (2005) “Stochastic modeling of daily Precipitation for Canada”. Atmos. Ocean, 43, 23-32, doi: 3137/ao.430102.
Li, J. C. Sun and F. F. Jin (2013) “NAO Implicated as a predictor of Northern Hemisphere Mean temperature multidecadal Variability” Geophys. Res. Let, 40, 05497-5502. doi: 10.1002/2013GL057877.
Li, Z., W. Z. Liu, X. C. Zhang, and F. L. Zheng, (2011) “Assessing the site specific impacts of climate change on hydrology, soil erosion and crop yields in the Loess Plateau of China”. Climatic Change, 105, 223–242, doi: 10.1007/s10584-010-9875-9.
Fathian, F., S. Morid, and E. Kahya, (2015) “Identification of trends in hydrological and climaticvariables in Urmia Lake basin, Iran”. Theor. Appl. Climatol., 119, 443–464, doi: 10.1007/s00704-014-1120-4.
Ricci, V., (2005) “Fitting distributions with R”. Rep., 35 pp. [Available online at]
RajibMaity. (2018) “Statistical Methods in Hydrology and Hydroclimatology” Springer Transactions in civil and Environmental Engineering,
Tariq, M. M and Abbas, A. I (2016). “Fitting probability Distributions of Annual Raifall in Sudan”. Journal of Engineering and computer sciences, Vol. 17, No 2.
Conway Declan, et al (2004).”Rainfall variability in East Africa: Implications for natural resources management and livelihoods”. The royal society.
Chen, H., S. Guo, X. Ch-yu, and V. P. Singh, (2007)“Historical temporal trends of hydro-limaticvariables and runoff response to climate variability and their relevance in waterresource management in the Hanjiang basin. J. Hydrol., 344, 171–184, doi: 10.1016/j.jhydrol.2007.06.034.
Eric, A. Davidson et al. (2000). “Effects of soil water content on soil respiration in forests and cattle pastures of Eastern Amazonia”. Kluwer Academic Publishers, Netherlands. Biogeochemistry 48: 53-69.
Eke, C. Osuij G. Amaeze and Nwosu, D. Felix. (2005). “Modeling of Nigeria’s Economic Growth Rate: A Probability Distribution Fitting Approach” Asian Journal of Probability and Statistics. Vol. 2, No. 1. Pp 1-17.
Nsubuga, F. W. N.. Olwoch, J. M. and Rautenbach, C. J. dew. (2011).“Climate trends at Namulonge in Uganda”. Journal of Geography and Geology, Vol. 3 No. 1, pp. 119-131.
Sambe, N. L. Adeofun, O. C. and Dachung, G.(2018). “The Economic and Ecological Effects of Deforestation on the Nigerian Environment.”Asian Journal of Advanced Research and Reports, 1 (2), 1-25. Retrieved from
Valli, M., S. S. Kotapati, and V. M. K. Iyyanki, (2013) Analysis of Precipitation Concentration Indexand Rainfall Prediction in various Agro-Climatic Zones of Andhra Pradesh, India. Int. Res. J. Environ. Sci., 2, 53–61.
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