Evolution of the Southern Kenya Rift from Miocene to present with a focus on the Magadi area

The Kenya (a.k.a., Gregory) Rift is a geologically active area located within the eastern branch of the larger East African Rift System (EARS). The study area is located in the southern Kenya Rift between 1° South and the Kenya-Tanzania border (covering approximately 1.5 square degrees, semi-centered on Lake Magadi) and is predominantly filled with extrusive igneous rocks (mostly basalts, phonolites and trachytes) of Miocene age or younger. Sediments are thin, less than 1.5Ma, and are confined to small grabens. The EARS can serve both as an analogue for ancient continental rifting and as a modern laboratory to observe the geologic processes responsible for rifting. This study demonstrates that vintage (as in older, quality maps published by the Kenya Geological Survey, that may be outdated based on newer findings) quarter-degree maps can be successfully combined with recently published data, and used to interpret satellite (mainly Landsat 7) images to produce versatile, updated digital maps. The study area has been remapped using this procedure and although it covers a large area, the mapping retains a quadrangle level of detail. Additionally, all geologic mapping elements (formations, faults, etc.) have been correlated across older map boundaries so that geologic units don't end artificially at degree boundaries within the study area. These elements have also been saved as individual digital files to facilitate future analysis. A series of maps showing the evolution of the southern Kenya rift from the Miocene to the present was created by combining the updated geologic map with age dates for geologic formations and fault displacements. Over 200 age dates covering the entire length of the Kenya Rift have been compiled for this study, and 6 paleo-maps were constructed to demonstrate the evolution of the area, starting with the eruption of the Kishalduga and Lisudwa melanephelinites onto the metamorphic basement around 15Ma. These eruptions occurred before the initial rift faulting and were followed by a massive eruption of phonolites between 13-10 Ma that covered most of the Kenya dome. This was followed by a period of relative quiescence, until the initial faulting defined the western boundary of the rift around 7Ma. The resulting graben was asymmetrical until corresponding faults to the east developed around 3Ma. The rift valley was flooded by basalts and trachytes between 3Ma and 700ka, after which the volcanic activity slowed to a near halt. Since 700ka most of the deposition has been comprised of sediments, mainly from lakes occupying the various basins in the area. The main results of this study are, in addition to a detailed interpretation of the rift development, a new geologic map that correlates dozens of formations across old map boundaries and a compilation of over 300 age dates. Specific products include paleomaps, tables of fault timing and displacement, and volume estimates of volcanic formations. The study concludes with a generalization of the present environment at Magadi including discussions of lagoon chemistry, mantle gases in relation to the trona deposit, and biology of the hot springs. Several biologic samples were collected during the 2006 field season in an attempt to characterize the organisms that are commonly seen in the present Lake Magadi environment. Samples were selected to represent the different, distinctive forms that are found in the hotsprings. Each sample had it own distinctive growth habit, and analysis showed that each was formed by a different cyanobacterial. Actual algae was rare in the collected samples, and represented by a few scattered diatoms.

PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.