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.
| Published in | Earth Sciences (Volume 9, Issue 2) |
| DOI | 10.11648/j.earth.20200902.13 |
| Page(s) | 65-75 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2020. Published by Science Publishing Group |
Hydroclimatilogy, Probability Distributions, Climate Variability, Water Resources
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APA Style
Ausi Abubakar Ssentongo, Nsubuga Francis Waswa, Daniel Darkey. (2020). Hydro-climatology Characterization of Degraded Lwamunda Forest Catchment Based on Probability Distributions. Earth Sciences, 9(2), 65-75. https://doi.org/10.11648/j.earth.20200902.13
ACS Style
Ausi Abubakar Ssentongo; Nsubuga Francis Waswa; Daniel Darkey. Hydro-climatology Characterization of Degraded Lwamunda Forest Catchment Based on Probability Distributions. Earth Sci. 2020, 9(2), 65-75. doi: 10.11648/j.earth.20200902.13
AMA Style
Ausi Abubakar Ssentongo, Nsubuga Francis Waswa, Daniel Darkey. Hydro-climatology Characterization of Degraded Lwamunda Forest Catchment Based on Probability Distributions. Earth Sci. 2020;9(2):65-75. doi: 10.11648/j.earth.20200902.13
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Download@article{10.11648/j.earth.20200902.13,
author = {Ausi Abubakar Ssentongo and Nsubuga Francis Waswa and Daniel Darkey},
title = {Hydro-climatology Characterization of Degraded Lwamunda Forest Catchment Based on Probability Distributions},
journal = {Earth Sciences},
volume = {9},
number = {2},
pages = {65-75},
doi = {10.11648/j.earth.20200902.13},
url = {https://doi.org/10.11648/j.earth.20200902.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20200902.13},
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.},
year = {2020}
}
TY - JOUR T1 - Hydro-climatology Characterization of Degraded Lwamunda Forest Catchment Based on Probability Distributions AU - Ausi Abubakar Ssentongo AU - Nsubuga Francis Waswa AU - Daniel Darkey Y1 - 2020/03/17 PY - 2020 N1 - https://doi.org/10.11648/j.earth.20200902.13 DO - 10.11648/j.earth.20200902.13 T2 - Earth Sciences JF - Earth Sciences JO - Earth Sciences SP - 65 EP - 75 PB - Science Publishing Group SN - 2328-5982 UR - https://doi.org/10.11648/j.earth.20200902.13 AB - 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. VL - 9 IS - 2 ER -
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