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Estimating water storage changes and sink terms in Volta Basin from satellite missions

Water Science and Engineering 2014年第1期7卷 5-16页

作者： Vagner G.FERREIRA samuel ato andam-akorful Xiu-feng HE Ru-ya XIAO School of Earth Sciences and Engineering Hohai University Department of Geomatic Engineering Kwame Nkrumah University of Science and Technology Institute of Satellite Navigation and Spatial Information Systems Hohai University

The insufficiency of distributed in situ hydrological measurements is a major challenge for hydrological studies in many regions of the world. Satellite missions such as the Gravity Recovery and Climate Experiment （GRACE） and the Tropical Rainfall Measurement Mission （TRMM） can be used to improve our understanding of water resources beyond surface water in poorly gauged basins. In this study we combined GRACE and TRMM to investigate monthly estimates of evaporation plus runoff （sink terms） using the water balance equation for the period from January 2005 to December 2010 within the Volta Basin. These estimates have been validated by comparison with time series of sink terms （evaporation plus surface and subsurface runoff） from the Global Land Data Assimilation System （GLDAS）. The results, for the period under consideration, show strong agreement between both time series, with a root mean square error （RMSE） of 20.2 ram/month （0.67 mm/d） and a correlation coefficient of 0.85. This illustrates the ability of GRACE to predict hydrological quantities, e.g. evaporation, in the Volta Basin. The water storage change data from GRACE and precipitation data from TRMM all show qualitative agreement, with evidence of basin saturation at approximately 73 mm in the equivalent water column at the annual and semi-annual time scales.

关键词： equivalent water column," evaporation," GLDAS GRACE," hydro-geodesy TRMM

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End-Member Selection in Two-Component Isotope-Based Hydrograph Separation

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Open Journal of Modern Hydrology 2019年第2期9卷 41-53页

作者： samuel Bansah Jonathan Quaye-Ballard samuel andam-akorful Edward Bam Geophrey K. Anornu Department of Geological Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana Isotope Hydrology Research Program (IHReP) Kumasi Ghana Department of Geological Sciences University of Manitoba Winnipeg Canada Manitoba’s Watershed Systems Research Program Manitoba Canada Center for Earth Observation Science University of Manitoba Winnipeg Manitoba Canada Department of Geomatic Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana Global Institute of Water Security University of Saskatchewan Saskatoon Canada Department of Civil Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana

The science that underpins our knowledge and understanding of Isotope-Based Hydrograph separation (IHS) has gained grounds, over the last few decades, in the identification of streamflow sources. However, challenges still exist in identifying appropriate tracers and the right combination of end-members for the IHS process. In a two-component IHS analysis, the application of the dual isotopes tracers, δ18O and (or) δ2H, is regarded as the simplest method. We undertook an IHS study within a nested system of eight Prairie watersheds located in South central Manitoba, Canada. The work evaluated about 17,000 results emanating from the application of a combination of two potential tracers (δ18O and δ2H) and eight each of potential “old” and “new” water end-members in a two-component IHS process. The outcome showed occurrences of many mathematically possible but hydrologically unacceptable IHS results. The observation was particularly predominant within relatively larger perennial sub-catchments of the watershed. It is also shown that inter-site sub-catchment isotopic end-member transferability is possible within watersheds of similar physio-hydrographic characteristics. We suggest that a careful evaluation of the physio-hydrographic characteristics of catchments be considered in IHS studies in addition to the recommended guidelines in the selection of tracers and end-members.

关键词： Hydrograph Separation Oxygen-18 Deuterium Stable Isotopes End-Member Definitions Canadian Prairies

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Biomass Estimation Models for Cocoa (Theobroma cacao) Plantations in Ghana, West Africa

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Open Journal of Applied Sciences 2023年第9期13卷 1588-1618页

作者： Emmanuel Donkor Stephen Adu-Bredu Edward Matthew Osei Jnr samuel A. andam-akorful Yakubu Mohammed Resource Management Support Centre of Forestry Commission Kumasi Ghana CSIR-Forestry Research Institute of Ghana Kumasi Ghana Department of Geomatic Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana

The role of cocoa systems for climate change mitigation and adaptation has increased substantially because of their capability to trap carbon dioxide from the atmosphere and deposited in the cocoa trees as carbon. Development of aboveground biomass (AGB) models for cocoa plantations is crucial for accurate estimation of carbon stocks in the cocoa systems, however, allometric models for estimating AGB for cocoa plantations remain a challenge for cocoa producing countries in Sub-Saharan Africa especially Ghana. The aim of this study is to develop allometric model that can be used for the estimation of AGB for cocoa plantations in Ghana, as well as West Africa. Destructive sampling was carried out on 110 cocoa trees obtained from the cocoa rehabilitation exercise for the development of the allometric models. Diameter at breast height (D), total tree height (H) and wood density (ρ) were used as predictors to develop seven models. The best model was selected based on coefficient of determination (R2), index of agreement (IA), root mean squared error (RMSE), bias (E%), mean absolute error (MAE) and corrected akaike information criterion (AICC) and percentage relative standard error (PRSE) of the estimated parameters. The selected model, which was the one with the predictors D and ρ, was given as;AGB = 0.7217ρ(D2)0.921. It was compared with the Yuliasmara et al. (2009) cocoa model using equivalence test and paired sample t-test. The two models were found to be equivalent within ±10% of their mean predictions (p < 0.0001) for one-tailed tests for both lower and upper limits, while the paired sample t-test rejected the null hypothesis with mean difference of 14.16 kg between the two models. This study is significant because it has provided a model to estimate AGB for the cocoa plantations in Ghana which is very important for the Ghana Cocoa-Forest REDD+ Programme and also can be used by other West African cocoa producing

关键词： Carbon Stocks Diameter at Breast Height Wood Density Tree Height Cocoa Landscape

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Application of Parametric and Non Parametric Classifiers for Assessing Land Use/Land Cover Categories in Cocoa Landscape of Juaboso and Bia West Districts of Ghana

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Journal of Geoscience and Environment Protection 2022年第11期10卷 265-281页

作者： Emmanuel Donkor Edward Matthew Osei Jnr Stephen Adu-Bredu samuel A. andam-akorful Efiba Vidda Senkyire Kwarteng Lily Lisa Yevugah Resource Management Support Centre of Forestry Commission Kumasi Ghana Kwame Nkrumah University of Science and Technology Kumasi Ghana Forestry Research Institute of Ghana Kumasi Ghana University of Energy and Natural Resources Sunyani Ghana

Satellite image classification has been used for long time in the field of remote sensing since classification results are used in environmental research, agriculture, climate change and natural resource management. The cocoa landscape of Ghana is complex and diverse in nature, composing of mixture of closed forest, open forest, settlements, croplands and cocoa farms which make mapping the landscape difficult. The purpose of this research is to assess and compare the classification performances of three machine learning classifiers: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and a statistical classification algorithm: Maximum Likelihood (ML) to know which classifier is best suited for mapping the cocoa landscape of Ghana using Juaboso and Bia West districts of Ghana as study area. A representative sampling approach was adopted to collect 1246 sample points for the various Land Use/Land Cover (LULC) types. These sample points were divided at random into 869 which form 70% for classification and 377 which constitute 30% of the total sample points for validation. The Stacked sentinel-2 image, classification data and validation data storing the identities of the LULC classes were imported in R to run supervised classification for each classifier. The classification results show that the highest overall accuracy and kappa statistics were produced by the support vector machine (86.47%, 0.7902);next is the artificial neural network (85.15%, 0.7700), followed by the random forest (84.08%, 0.7559) and finally the maximum likelihood (78.51%, 0.6668). The final LULC map produced under this study can be used to monitor cocoa driven deforestation especially in the gazetted forest and game reserves. This map will also be very useful in the national forest monitoring framework for the REDD + cocoa landscape project.

关键词： Support Vector Machine Random Forest Artificial Neural Network Maximum Likelihood Image Classification Cocoa Landscape

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Biomass Estimation Models for Cocoa (Theobroma cacao ) Plantations in Ghana,West Africa

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Open Journal of Applied Sciences（应用科学） 2023年第9期

作者： Emmanuel Donkor Stephen Adu-Bredu Edward Matthew Osei Jnr samuel A.andam-akorful Yakubu Mohammed

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