River Conveyance Analysis in a Data Sparse and a Data Rich Region: Case Studies in Kenya and Switzerland Master’s Thesis Faculty of Science University of Bern presented by Danielle Huser 2016 Supervisor: Prof. Dr. Rolf Weingartner Institute of Geography and Oeschger Centre for Climate Change Research Co-Supervisor: Dr. Andreas Zischg Institute of Geography and Oeschger Centre for Climate Change Research Advisor: Dr. Hanspeter Liniger Institute of Geography i Abstract This master thesis addresses two cases studies with rather different initial condition. First, it aims to establish stage discharge relations in data-sparse regions in the lower Ewaso Ng’rio Basin in Kenya, and secondly, it proceeds to a weak point analysis along the main rivers of the Bernese Oberland in Switzerland. Regardless of their geographical distance, for both re- gions with their respective issues a hydrodynamic 1D simulations were conducted with the software Basement, providing simulations of river flow based on measured cross sections. In arid regions water scarcity is becoming a threat to more and more people. The Ewaso Ng’rio Basin (64 000 km2), located around the Mount Kenya is subjected to increasing pres- sure on water resources because of rising demands for irrigation, domestic purposes, and livestock in the upper basin. In the lower basin, users suffer from even more pronounced water scarcity, because they have secondary access to the rivers. Research on hydrological processes is limited since gauging stations (RGS) were missing up to this study. In order to overcome that limitation, this study aims to establish stage-discharge relations for four sites in the lower Ewaso Ng’iro Basin by relying on newly installed RGS providing water level data. The here developped methodological approach is adapted to the scarcely distributed measurement equipment. To establish stage-discharge relations, the river sites were sur- veyed with a dumpy level to collect the necessary input variable geometry, and Manning- Strickler values for the 1D simulations in Basement (Vetsch et al., 2015b). The hydrodynamic model is based on the Saint-Venant and Manning-Strickler equations. The stage-discharge relation was simulated with Basement without a calibration of the model. The curve fitting and analysis of the relation between stage and discharge was evaluated with the statistical program R, using a non-linear model based on a power function. For all four river sites, the survey method enabled simulations of river flows in Basement. The curve mirrored the relations rather well with some deviations at low flow. Despite of probably high and un- quantifiable uncertainties of data collected in field surveys, this study nevertheless provides a first step to a more comprehensive understanding of the magnitude and timing of the river flow in the lower Ewaso Ng’rio Basin. Further research should focus on calibrating the stage discharge relation of the four sites. In Switzerland, climate change is likely to increase the frequency of extreme flow events. Hydrology has extensively been studied in terms of frequency of flood events and of weak points along the rivers. The focus of these analyses lies on single river sections. Alterna- tively, this study proposes to evaluate weak points based on the bank-full discharge with associated return period at measured cross sections along the main rivers of the Bernese Oberland. This simple approach can be applied identically to all the rivers and allows for the first time a comparison on large scale. For each cross section along the rivers Hasliaare, Aare Thun-Bern, Simme, Kander and Lütschine, discharges of flood events with return pe- riods of 10, 30, 50, 100, and 300 years (HQx) were identified. With the help of known HQ10, HQ30, HQ50, HQ100 and HQ300, discharge rates were calculated at the RGS stations. The dis- charge rates were then extrapolated or interpolated over the catchment size of all the cross sections along the entire river. HQ values for different return periods were compared at some reference points with estimations provided by the HQx_meso_CH tool (Barben, 2001) and the PREVAH_regHQ tool (Viviroli and Weingartner, 2012). The bank-full discharge, or ii what discharge fills the channel to the top, was evaluated for each cross section by 1D sim- ulations in Basement for each river. The models were calibrated on the basis of measured stage-discharge relations. The results of the estimations of HQ10, HQ30, HQ50, HQ100 and HQ300 seemed to depend on the number and the position of the RGS: the more stations and the higher upstream, the better the estimation. Identifiable changes of the HQx along the river are caused by tributary inflow. Low bank-full discharge values could be identified at single or conglomerated cross sections, and they could be backtracked to the topographic situation. Identified weak points were mostly located along non-populated areas. Inter- estingly, some areas with extensive construction measures against floods still exhibited low return periods. The rather simple and time saving methodological approach of extrapolat- ing HQx values at the site of the RGS and of running a 1D simulation in Basement produced results comparable with literature, though some deviations occurred. This is remarkable, as, compared to other studies, the covered area was large and the riverbed was reconstructed from measured cross sections. Because the model is simple and relies on available data, it could be extended countrywide. This would further favour a better comparison between different rivers, because results would be based on one identical methodological approach – something that is currently lacking in today’s procedures in flood assessment. As the ad- vantages of the model are promising, it is recommended to conduct further studies in order to reach more comprehensive calibrations and to conduct validations. iii Acknowledgement I would like to thank to...... ....Rolf Weingartner, who gave me the opportunity to write this thesis and the support to find a second topic to complete the thesis. ....Hanspeter Liniger for introducing us in Nanyuki Kenya during the 2 week excursion and the support by phone during the 3 month field work. ....the CETRAD water team, for their assisstence and encouragement during my fieldwork. ....Andras Zischg for the support by mail during the time in Kenya, for being as flexible to come up with a second topic, and for the encouragement, the helpful input, feedback, and the informative field trips in the Bernese Oberland. ....Julia Stefanovic and Jérôme Léchot to proof-reading my thesis ....My family and friends for their encouragement throughout the working process. Espe- cially, to Noemi Imfeld and Fabienne Kaufmann for the positive energy, the confidence and motivation, the scientific and no-scientific discussions and the nice dinners during the time we shared in Nankyuki, Kenya iv Contents 1 Introduction 1 1.1 Background . 1 1.2 Outline . 2 1.3 Software Basement: Basechain 1D . 2 1.3.1 Hydraulic Background . 3 1.3.1.1 Flow Equation . 3 1.3.1.2 Numerical Solution of the SVE . 4 1.3.2 Modelling Procedure . 5 1.3.2.1 Boundary Condition . 6 1.3.2.2 Calibration . 6 2 Determination of the Stage-Discharge Relations in a Data Sparse Region in Lower Ewaso Ng’iro Basin Kenya 7 2.1 Introduction . 7 2.1.1 Problem Description . 7 2.1.2 Objectives and Research Questions . 8 2.1.3 Study Area . 9 2.1.3.1 Characteristics of the Survey Sites . 12 2.2 Methods . 13 2.2.1 Field Survey . 13 2.2.1.1 Channel Geometry . 14 2.2.1.2 Roughness Coefficient . 15 2.2.2 Water level-Discharge Simulation . 15 2.2.3 Establishing Rating Curves . 16 2.2.4 Curve Fitting . 16 2.2.5 Uncertainties and Sensitivity Analysis . 17 2.3 Results . 18 2.3.1 Sereolevi . 19 2.3.1.1 Overview Sereolevi . 19 2.3.1.2 Stage-Discharge Relation and Error Sources . 20 2.3.1.3 Rating Curve and Power Function . 21 2.3.2 Merille . 22 2.3.2.1 Overview Merille . 22 2.3.2.2 Stage-Discharge Relation and Error Sources . 23 2.3.2.3 Rating Curve and Power Function . 24 2.3.3 Melgis . 25 2.3.3.1 Overview Melgis . 25 2.3.3.2 Stage-Discharge Relation and Error Sources . 26 2.3.3.3 Rating Curve and Power Function . 27 v 2.3.4 Habaswein . 28 2.3.4.1 Overview Habaswein . 28 2.3.4.2 Stage-Discharge Relation and Error Sources . 29 2.3.4.3 Rating Curve and Power Function . 30 2.4 Discussion . 31 2.5 Conclusion . 33 3 Weak Point Analysis along the Main Rivers of Bernese Oberland Switzerland 34 3.1 Introduction . 34 3.1.1 Problem Description . 34 3.1.2 Objectives and Research Questions . 36 3.1.3 Study Area . 37 3.1.3.1 Survey Site . 38 3.2 Methodology . 41 3.2.1 Data . 41 3.2.2 Flood Assessment . 42 3.2.2.1 Catchment Size . 43 3.2.2.2 Calculation of the HQx along the Rivers . 44 3.2.2.3 Plausibility . 45 3.2.3 Determination of the Bank-full Discharge . 46 3.2.3.1 Pre-Processing . 47 3.2.3.2 Calibration . 48 3.2.3.3 Simulations and Determination of the Bank-full Discharge . 49 3.2.3.4 Plausibility . 49 3.3 Results . 50 3.3.1 Floods with Return Periods of 10, 30, 50, 100 and 300 Years . 50 3.3.1.1 Hasliaare . 50 3.3.1.2 Aare Thun-Bern . 52 3.3.1.3 Simme . 53 3.3.1.4 Kander . 55 3.3.1.5 Schwarze Lütschine . 57 3.3.1.6 Weisse Lütschine . 59 3.3.1.7 Vereinte Lütschine .