@article{AsareKyeiForkuorVenus2015, author = {Asare-Kyei, Daniel and Forkuor, Gerald and Venus, Valentijn}, title = {Modeling Flood Hazard Zones at the Sub-District Level with the Rational Model Integrated with GIS and Remote Sensing Approaches}, series = {Water}, volume = {7}, journal = {Water}, doi = {10.3390/w7073531}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-151581}, pages = {3531 -- 3564}, year = {2015}, abstract = {Robust risk assessment requires accurate flood intensity area mapping to allow for the identification of populations and elements at risk. However, available flood maps in West Africa lack spatial variability while global datasets have resolutions too coarse to be relevant for local scale risk assessment. Consequently, local disaster managers are forced to use traditional methods such as watermarks on buildings and media reports to identify flood hazard areas. In this study, remote sensing and Geographic Information System (GIS) techniques were combined with hydrological and statistical models to delineate the spatial limits of flood hazard zones in selected communities in Ghana, Burkina Faso and Benin. The approach involves estimating peak runoff concentrations at different elevations and then applying statistical methods to develop a Flood Hazard Index (FHI). Results show that about half of the study areas fall into high intensity flood zones. Empirical validation using statistical confusion matrix and the principles of Participatory GIS show that flood hazard areas could be mapped at an accuracy ranging from 77\% to 81\%. This was supported with local expert knowledge which accurately classified 79\% of communities deemed to be highly susceptible to flood hazard. The results will assist disaster managers to reduce the risk to flood disasters at the community level where risk outcomes are first materialized.}, language = {en} } @phdthesis{Konrad2015, author = {Konrad, Tillmann}, title = {Governance of Protected Areas in West Africa - The case of the W-Arly-Pendjari (WAP) Complex in Benin and Burkina Faso}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-115331}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2015}, abstract = {Protected areas are the central strategy for preserving biodiversity in the face of overexploitation and global change. To ensure their long-term survival, however, these areas may not be regarded as last havens of wilderness, but as complex social-ecological systems. Modern approaches of protected area (PA) management support this view by balancing conservation and development issues in a sustainable way and adapted to the local context. However, success of these strategies in achieving their aims so far remains limited. This study therefore aimed at analysing processes and outcomes of PA co-management approaches implemented in a large transfrontier conservation area in West Africa. The W-Arly-Pendjari (WAP) complex spans over more than 30.000 square km in Benin, Burkina Faso and Niger and is composed of approximately 20 subunits. Due to national legal and administrative variety as well as a high diversity of local (project) implementation approaches, the general setting resembled a quasi-experimental design facilitating comparative studies. A mix of quantitative (e.g. survey of 549 households) and qualitative (e.g. expert interviews, literature review) methods was used to evaluate the institutional and organisational differences of PA management approaches implemented in the different parts of WAP belonging to Benin and Burkina Faso. I included an analysis of contextual factors (e.g. land-cover-change) and ecological data, but concentrated on the role of local resource users within the co-management arrangements and the effectiveness of governance regimes to deliver positive socio-economic outputs. Exploring the question whether promotion of development in PA surroundings indeed stipulates conservation success (and vice versa) remained challenging: the lack of sound ecological data, a general mismatch of spatial scale in existing data sets, as well as the high complexity of realities on the ground made me refrain from using simplified proxy indicators and (statistical) modelling approaches. I found that the Sudano-Sahelian context is a very difficult one for the implementation of effective participation approaches in the short-term. Political, demographic, socio-economic as well as ecological factors generated a very dynamic situation characterized by limited financial and natural resources as well as weak institutional and organisational settings. Arenas of interaction were often marked rather by a high degree of distrust and competition than by cooperation among actors. Amid all rhetoric, participation in most cases was hence limited to the transfer of (sparse) information, regulated resource access and financial funds. Options for participation of local resource users in decision-making arenas were generally scarce. Underlying processes were dominated by opacity and often low accountability of actors on all levels. Negative, but also positive affection of local residents by PA existence and management hence was high. Governance regimes of the complex performed very differently with regard to their ability of effectively empowering local village participatory bodies (vpb), generating and distributing benefits to individuals and village communities as well as providing mechanisms of conflict resolution. People around Pendjari enjoyed a relative wealth of high value benefits, while negative impacts caused by human-wildlife conflicts were widespread around the complex. Autochthonous farmers usually were better integrated in incentive schemes than were newcomers or herders. While there was functional separation of actors' roles in all parts of WAP, these roles differed significantly between blocks. Existence and functioning of village participatory bodies ameliorated the situation for local resource users fundamentally, as they acted as cut-points between different networks (governmental hierarchies, private concessionaires and local resource users). Vpbs in the Pendjari region proved to be most advanced in their capacity to push resource users' claims in action arenas on the micro-level. Via their union, these associations also managed to impact arenas on the meso- and the macro scale. Project interventions often had catalyst functions to empower local resource users and their vbps. However, they also contributed to social imbalance and intra-organisational competition. My results represent a snapshot of an ongoing process to establish effective co-governance regimes in the WAP-area. Though I identified a large scope of shortcomings, there were also very promising initiatives underway. This work is therefore meant to foster future research and further positive development by giving guidance scholars and decision-makers form the local to the global level alike.}, subject = {Gesch{\"u}tzte Natur}, language = {en} } @article{ZoungranaConradAmekudzietal.2015, author = {Zoungrana, Benewinde Jean-Bosco and Conrad, Christopher and Amekudzi, Leonard K. and Thiel, Michael and Dapola Da, Evariste and Forkuor, Gerald and L{\"o}w, Fabian}, title = {Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa}, series = {Remote Sensing}, volume = {7}, journal = {Remote Sensing}, number = {9}, doi = {10.3390/rs70912076}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-125866}, pages = {12076-12102}, year = {2015}, abstract = {Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates are essential for a better understanding of LULCC processes. This study aimed at comparing mono-temporal and multi-temporal LULC classifications as well as their combination with ancillary data and to determine LULCC across the heterogeneous landscape of southwest Burkina Faso using accurate classification results. Landsat data (1999, 2006 and 2011) and ancillary data served as input features for the random forest classifier algorithm. Five LULC classes were identified: woodland, mixed vegetation, bare surface, water and agricultural area. A reference database was established using different sources including high-resolution images, aerial photo and field data. LULCC and LULC classification accuracies, area and area uncertainty were computed based on the method of adjusted error matrices. The results revealed that multi-temporal classification significantly outperformed those solely based on mono-temporal data in the study area. However, combining mono-temporal imagery and ancillary data for LULC classification had the same accuracy level as multi-temporal classification which is an indication that this combination is an efficient alternative to multi-temporal classification in the study region, where cloud free images are rare. The LULCC map obtained had an overall accuracy of 92\%. Natural vegetation loss was estimated to be 17.9\% ± 2.5\% between 1999 and 2011. The study area experienced an increase in agricultural area and bare surface at the expense of woodland and mixed vegetation, which attests to the ongoing deforestation. These results can serve as means of regional and global land cover products validation, as they provide a new validated data set with uncertainty estimates in heterogeneous ecosystems prone to classification errors.}, language = {en} } @phdthesis{Awoye2015, author = {Awoye, Oy{\´e}monbad{\´e} Herv{\´e} Rodrigue}, title = {Implications of future climate change on agricultural production in tropical West Africa: evidence from the Republic of Benin}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-122887}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2015}, abstract = {Environmental interlinked problems such as human-induced land cover change, water scarcity, loss in soil fertility, and anthropogenic climate change are expected to affect the viability of agriculture and increase food insecurity in many developing countries. Climate change is certainly the most serious of these challenges for the twenty-first century. The poorest regions of the world - tropical West Africa included - are the most vulnerable due to their high dependence on climate and weather sensitive activities such as agriculture, and the widespread poverty that limits the institutional and economic capacities to adapt to the new stresses brought about by climate change. Climate change is already acting negatively on the poor smallholders of tropical West Africa whose livelihoods dependent mainly on rain-fed agriculture that remains the cornerstone of the economy in the region. Adaptation of the agricultural systems to climate change effects is, therefore, crucial to secure the livelihoods of these rural communities. Since information is a key for decision-making, it is important to provide well-founded information on the magnitude of the impacts in order to design appropriate and sustainable adaptation strategies. Considering the case of agricultural production in the Republic of Benin, this study aims at using large-scale climatic predictors to assess the potential impacts of past and future climate change on agricultural productivity at a country scale in West Africa. Climate signals from large-scale circulation were used because state-of-the art regional climate models (RCM) still do not perfectly resolve synoptic and mesoscale convective processes. It was hypothesised that in rain-fed systems with low investments in agricultural inputs, yield variations are widely governed by climatic factors. Starting with pineapple, a perennial fruit crops, the study further considered some annual crops such as cotton in the group of fibre crops, maize, sorghum and rice in the group of cereals, cowpeas and groundnuts belonging to the legume crops, and cassava and yams which are root and tuber crops. Thus the selected crops represented the three known groups of photosynthetic pathways (i.e. CAM, C3, and C4 plants). In the study, use was made of the historical agricultural yield statistics for the Republic of Benin, observed precipitation and mean near-surface air temperature data from the Climatic Research Unit (CRU TS 3.1) and the corresponding variables simulated by the regional climate model (RCM) REMO. REMO RCM was driven at its boundaries by the global climate model ECHAM 5. Simulations with different greenhouse gas concentrations (SRES-A1B and B1 emission scenarios) and transient land cover change scenarios for present-day and future conditions were considered. The CRU data were submitted to empirical orthogonal functions analysis over the north hemispheric part of Africa to obtain large-scale observed climate predictors and associated consistent variability modes. REMO RCM data for the same region were projected on the derived climate patterns to get simulated climate predictors. By means of cross-validated Model Output Statistics (MOS) approach combined with Bayesian model averaging (BMA) techniques, the observed climate predictors and the crop predictand were further on used to derive robust statistical relationships. The robust statistical crop models perform well with high goodness-of-fit coefficients (e.g. for all combined crop models: 0.49 ≤ R2 ≤ 0.99; 0.28 ≤ Brier-Skill-Score ≤ 0.90). Provided that REMO RCM captures the main features of the real African climate system and thus is able to reproduce its inter-annual variability, the time-independent statistical transfer functions were then used to translate future climate change signal from the simulated climate predictors into attainable crop yields/crop yield changes. The results confirm that precipitation and air temperature governed agricultural production in Benin in general, and particularly, pineapple yield variations are mainly influenced by temperature. Furthermore, the projected yield changes under future anthropogenic climate change during the first-half of the 21st century amount up to -12.5\% for both maize and groundnuts, and -11\%, -29\%, -33\% for pineapple, cassava, and cowpeas respectively. Meanwhile yield gain of up to +10\% for sorghum and yams, +24\% for cotton, and +39\% for rice are expected. Over the time period 2001 - 2050, on average the future yield changes range between -3\% and -13\% under REMO SRES-B1 (GHG)+LCC, -2\% and -11\% under REMO SRES-A1B (GHG only),and -3\% and -14\% under REMO SRES-A1B (GHG)+LCC for pineapple, maize, sorghum, groundnuts, cowpeas and cassava. In the meantime for yams, cotton and rice, the average yield gains lie in interval of about +2\% to +7\% under REMO SRES-B1 (GHG)+LCC, +0.1\% and +12\% under REMO SRES-A1B (GHG only), and +3\% and +10\% under REMO SRES-A1B (GHG)+LCC. For sorghum, although the long-term average future yield depicts a reduction there are tendencies towards increasing yields in the future. The results also reveal that the increases in mean air temperature more than the changes in precipitation patterns are responsible for the projected yield changes. As well the results suggest that the reductions in pineapple yields cannot be attributed to the land cover/land use changes across sub-Saharan Africa. The production of groundnuts and in particular yams and cotton will profit from the on-going land use/land cover changes while the other crops will face detrimental effects. Henceforth, policymakers should take effective measures to limit the on-going land degradation processes and all other anthropogenic actions responsible for temperature increase. Biotechnological improvement of the cultivated crop varieties towards development of set of seed varieties adapted to hotter and dry conditions should be included in the breeding pipeline programs. Amongst other solutions, application of appropriate climate-smart agricultural practices and conservation agriculture are also required to offset the negative impacts of climate change in agriculture.}, subject = {Benin}, language = {en} }