http://197.159.135.214/jspui/handle/123456789/25 2026-04-05T15:36:34Z http://197.159.135.214/jspui/handle/123456789/681 Validation of satellite soil moisture in the absence of in situ soil moisture: the case of the Tropical Yankin Basin Badou, Djigbo F.; Diekkrüger, Bernd; Montzka, Carsten Soil moisture is known to be important in hydrology, agronomy, flood and drought forecasting. Acquisition of in situ soil moisture data is time consuming, costly, and does not cover the scale required for basin analysis. The consideration of remotely-sensed soil moisture is therefore promising. However, considering the limitations of satellite data, there is a need to check their validity prior to their utilization for impact studies. This, in turn, poses a problem in the absence of in situ soil moisture. The present study suggests a methodology for testing the validity of remotely-sensed soil moisture without in situ soil moisture. Hydrological models with a detailed soil moisture routine are calibrated and validated with measured stream flows. The most behavioural solutions of modelled soil moistures are averaged, and used as proxy measurements. This methodology was applied to the Yankin Basin (8,171 km2), a tributary of the Niger River Basin. The soil moistures of three hydrological models (UHP-HRU, SWAT and WaSiM) used as proxy were compared with the daily ESA-CCI soil moisture for a four year period (2005-2008). The coefficient of determination (R2), bias and visual inspection were used as quality criteria. A rather small bias ranging from -0.01cm3/cm3 (SWAT & UHP-HRU) to -0.04cm3/cm3 (WaSiM &UHP-HRU) was determined as well as good R2 varying between 0.71 (SWAT & UHP-HRU) and 0.81 (WaSiM & SWAT & UHP-HRU). The ESA-CCI soil moisture was therefore judged as reliable for the study area. More important, this research shows that averaging soil moistures from different hydrological models provides valuable proxy measurements for testing the reliability of satellite soil moistures. Research Article 2018-01-01T00:00:00Z http://197.159.135.214/jspui/handle/123456789/680 Potentials and Characteristics of Landsat Imagery in Relation to Land Use /Cover in Okitipupa Metropolis, Ondo State, Nigeria Tobore, A. O.; Oyerinde, G.; Senjobi, B.A.; Ogundiyi, T.O. Landsat satellite imagery plays a crucial role in providing information on land use/cover modifications on local, regional, and global scales, especially where aerial photographs are missing. Monitoring land -use changes from past to present tends to be time-consuming especially when dealing with groundtruth information. Determining the past and current land-use change on Earth's surface using Landsat imagery tends to be effective and efficient when high-resolution imagery is unavailable. This study employed the use of Landsat satellite imagery to assess the past and present land use/cover using supervised classification and Normalized Difference Vegetation Index (NDVI). The result of the supervised classification land use/cover showed that forest cover and woodland undergo rapid loss, while farmland, wetland, built-up, and waterbodies tend to experience gradual loss. The NDVI demonstrated that farmland and forest cover was the most affected land use/cover. Hence, land use/cover of the study area is affected by human activities, such as intensive farming, population size, and deforestation. Research Article 2019-01-01T00:00:00Z http://197.159.135.214/jspui/handle/123456789/679 Increasing Devastating Flood Events in West Africa: Who is to Blame? Badou, Félicien D.; Hounkpè, Jean; Yira, Yacouba; Ibrahim, Moussa; Bossa, Aymar Y. In recent years, climate change has become a major worldwide concern. Among its different manifestations in West Africa, flood events are the most devastating. This chapter pulls out the main causes of floods in West Africa from recently published research with the aim of serving as a guide in defining paramount actions to be taken to address flood issues in the region. Inadequate urban planning, poor land management and land occupation, especially in populated areas, amplify climate change impacts and increase flood risk. The situation is likely to worsen in the future. The effectiveness of current adaptation and mitigation measures is analysed and recommendations on possible follow-up actions are given. Given that the cost of inaction might exceed the cost of taking early action, this chapter launches an appeal for concrete actions for flood impacts mitigation in West Africa Research Article 2019-01-01T00:00:00Z http://197.159.135.214/jspui/handle/123456789/678 Heavy rainfall frequency analysis in the Benin section of the Niger and Volta Rivers basins: is the Gumbel’s distribution a one-size-fits-all model? Badou, Djigbo Félicien; Adango, Audrey; Hounkpè, Jean; Bossa, Aymar; Yira, Yacouba; Biao, Eliezer Iboukoun; Adounkpè, Julien; Alamou, Eric; Sintondji, Luc Ollivier C.; Afouda, Abel Akambi West African populations are increasingly exposed to heavy rainfall events which cause devastating floods. For the design of rainwater drainage facilities (to protect populations), practitioners systematically use the Gumbel distribution regardless of rainfall statistical behaviour. The objective of this study is twofold. The first is to update existing knowledge on heavy rainfall frequency analysis inWest Africa to check whether the systematic preference for Gumbel’s distribution is not misleading, and subsequently to quantify biases induced by the use of the Gumbel distribution on stations fitting other distributions. Annual maximum daily rainfall of 12 stations located in the Benin sections of the Niger and Volta Rivers’ basins covering a period of 96 years (1921–2016) were used. Five statistical distributions (Gumbel, GEV, Lognormal, Pearson type III, and Log-Pearson type III) were used for the frequency analysis and the most appropriate distribution was selected based on the Akaike (AIC) and Bayesian (BIC) criteria. The study shows that the Gumbel’s distribution best represents the data of 2=3 of the stations studied, while the remaining 1=3 of the stations fit better GEV, Lognormal, and Pearson type III distributions. The systematic application of Gumbel’s distribution for the frequency analysis of extreme rainfall is therefore misleading. For stations whose data best fit the other distributions, annual daily rainfall maxima were estimated both using these distributions and the Gumbel’s distribution for different return periods. Depending on the return period, results demonstrate that the use of the Gumbel distribution instead of these distributions leads to an overestimation (of up to C6:1 %) and an underestimation (of up to 􀀀45:9 %) of the annual daily rainfall maxima and therefore to an uncertain design of flood protection facilities. For better validity, the findings presented here should be tested on larger datasets. Research Article 2021-11-01T00:00:00Z