Browsing by Author "Laux, Patrick"

Now showing 1 - 1 of 1

Open Access
Statistical modeling of precipitation for agricultural planning in the Volta Basin of West Africa
(2009) Laux, Patrick; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing. habil.)
Water availability plays a vital role in the promotion of economic growth and reduction of poverty in the Volta Basin. Due to the increasing population pressure, there is a rapidly increasing demand for water. Climate change additionally impacts water availability and may aggravate water scarcity in the future. Agriculture is the major water consuming sector in the Volta Basin. High rainfall variability of the past often led to shortages in food supply and famines, especially in the Sahel. In such regions, where rainfall is limited to only few months per year, the exact determination of the rainy seasons’ onset is of crucial interest for farming management. Every year, farmers are faced with the question when to start sowing. Do the first rainfalls after the dry season resemble the onset of the rainy season or not? The farmers' seeds and effort will be lost if no significant rainfall follows within the following weeks after sowing. If they do not sow but the first rains were already part of the rainy season, valuable time for agricultural production is lost. Apparently, the benefits of a solution to this problem would be enormous. Therefore, one major goal of this thesis is to find relevant indicators to reliably predict the rainy season's onset. To archive this goal, two different strategies were followed within the context of the doctoral thesis: - The first strategy tackles the problem using solely the measured rainfall time series. Linear discriminant analysis and linear regression analysis were used to predict the onset of the rainy season. - The second strategy uses large-scale meteorological reanalysis fields from GCM output. A multi objective fuzzy logic-based classification algorithm is used to link large-scale meteorological conditions with the onset of the rainy season. Apart from the onset of the rainy season, different rainfall characteristics are analyzed, which are important for agricultural management. These are rainfall occurrence probability, rainfall amount and dry spell probability and are presented as risk maps, suitable for agricultural decision support in the Volta Basin. Drought analysis on regional scale is conducted. The Effective Drought Index is used to derive drought duration, drought intensity, and drought interarrival time. A bivariate Copula approach is therefore used to model the regional return periods of droughts using jointly drought duration and drought intensity. For the investigation of the impact of climate change on these agro-meteorological characteristics, local rainfall information on daily time scale is required. Especially in regions with weak infrastructure like the Volta Basin, all the applied methodologies are hampered by the fact that only little and incomplete meteorological information is available. Statistical downscaling of coarse resolved global circulation models (GCMs) in companion with stochastic rainfall simulation is applied alternatively to avoid these shortcomings. The performance of two different statistical approaches, a simple weather generator and a more sophisticated combined weather pattern classification and simulation approach are evaluated in the context of that doctoral thesis. For the latter, the performance is depending on various factors like e.g. the choice of the predictor(s), the location and size of the domain etc. The impact of climate change on rainfall variability in the Volta Basin is elaborated in terms of future rainy season onset dates and weather pattern frequencies using the A1B scenario driven ECHAM5 model output. For the future time slice 2011-2040, a drastic delay in the onset of the rainy season is expected. Wet and droughty weather patterns are expected to increase in the two northernmost regions of the Volta Basin. Finally, the impact of rainfall variability on crop yield is investigated via multiple linear regression analysis. Rainfall variability has been assessed in terms of the annual rainfall amount, the number of rainy days, the onset, cessation and length of the rainy season and the annual mean of the monthly averaged Effective Drought Index. Regression models, which explain up to 80% of the total variance of crop yield, could be established. It is found, that the annual precipitation amount is the dominant factor to estimate crop yield.