Abstract
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.
Highlights
The interaction between hydrological and climatological factors in tropical areas among others, determines the magnitude and quality of water resources available for both humans and other living creatures
The use of probability distribution models capable of reproducing the statistics of hydro-climatology data series is helpful in analyzing complex phenomena with compounding factors such as the interaction between land use change and water resources
The direction of forest cover change in the study area provides strong argument to assess the impact of land use change on water resources
Summary
The interaction between hydrological and climatological factors in tropical areas among others, determines the magnitude and quality of water resources available for both humans and other living creatures. In particular forests and water bodies are crucial in influencing the hydrological circle within the tropics. Management of these resources that are increasingly becoming scarce is a key factor for their sustainable use. With increasing pressure from population growth and climate change and its variability, management of these resources becomes a challenge. Any change in their use indirectly constrains their availability on temporal and spatial scales. Climate variability and land cover change like forest cover change are consistently associated with changes in water balance accompanied with changes in other hydrological systems, such as the changing patterns of rainfall, drainage density and spring flow
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