Accessible flood modeling for low-resource, data-scarce communities currently does not exist. This paper proposes using drone imagery to compensate for the lack of other flood modeling data (i.e. streamflow measurements). Three flood models were run for Dzaleka Refugee Camp, located in Dowa, Malawi. Two of the models (the Soil and Water Assessment Tool (SWAT) and the Hydrologic Engineering Center River Analysis System (HEC-RAS)) are commonly used hydrological-hydraulic based models. The third model, the Water Caused Erosion Patterns (WCEP) model, was proposed by the author to capitalize on the high-resolution drone imagery using geological-geomorphological information. The drone imagery used in this study has a resolution of 3.5cm and shows erosion patterns throughout the refugee camp. By comparing the erosion patterns to flow direction of the surface, the erosion patterns were determined to be water caused or not water caused, the erosion patterns considered water caused were defined as high-risk flood areas, creating the WCEP model. The three models were compared using locations of collapsed houses throughout the camp. It was found that the WCEP model represents the location of collapsed houses significantly better (misclassification rate below 17%) than the SWAT or HEC-RAS models (misclassification rate below 54%, and 67% respectively). The WCEP model was combined with the best hydrological-hydraulic model (SWAT) to create a hydrogeomorphological model which capitalizes on both the drone imagery and the hydrological process. / Master of Science / The negative impact flooding has on communities can be reduced through flood modeling. But commonly used flood models are not accessible to data-scarce communities because of the historical data the models require. This paper explores using aerial imagery taken by a drone to make-up for the lack of historical data at Dzaleka Refugee Camp located in Dowa, Malawi. Drone imagery has a very high spatial resolution (3.5cm), so it is able to provide a lot of details, including marks that show an increase of flooding in certain areas and elevation information. The flood model presented in this paper is created using the found flood marks in drone imagery. The presented model is then compared to two commonly used flood models, and all three flood models are compared to locations of houses that collapsed from flooding throughout the refugee camp. The model created using drone imagery did the best job predicting high-risk locations with misclassification rates below 17%. The drone imagery model was then combined with a commonly used model to create a more comprehensive flood model, capitalizing on all available data.

An increasing trend in both the number of disasters and affected people has been observed, especially during the second half of the 20th century. The physical, economic and social impact that natural hazards have had on a global scale has prompted an increasing interest of governments, international institutions and the academia. This has immensely contributed to improve the knowledge on the subject and has helped multiply the number of initiatives to reduce the negative consequences of natural hazards on people. The scale on which studies supporting disaster risk reduction (DRR) actions are performed is a critical parameter. Given that disasters are recognized to be place-dependent, studying the geographical distribution of disasters on a local scale is essential to make DRR practical and feasible for local authorities, organizations and civilians. However, studying disasters on the local scale is still a challenge due to the constraints posed by scarce data availability. Social vulnerability in many disaster-prone areas is however a pressing issue that needs to be swiftly addressed despite of the many limitations of data for such studies. This thesis explored methodological alternatives to study the geographical distribution of natural disasters and their potential causes in disaster-prone and data-scarce areas. The Samala River catchment in Guatemala was selected as a case study, which is representative of areas with high social vulnerability and data scarcity.  Exploratory methods to derive critical disaster information in such areas were constructed using the geographical and social data available for the study area. The hindrances posed by the available data were evaluated and the use of non-traditional datasets such as nightlights imagery to complement the available data were explored as a way of overcoming the observed limitations. The exploratory methods developed in this thesis aim at (a) deriving information on natural disasters under data-scarce circumstances, (b) exploring the correlation between the spatial distribution of natural disasters and the physical context in order to look for causalities, (c) using open data to study the social context as a potential cause of disasters in data-scarce areas, and (d) mapping vulnerabilities to support actions for disaster risk reduction. Although the available data for the case study was limited in quantity and quality and many sources of uncertainty exist in the proposed methods, this thesis argues that the potential contribution to the development of DRR on a local scale is more important than the identified drawbacks. The use of non-traditional data such as remotely sensed imagery made it possible to derive information on the occurrences of disasters and, in particular, causal relationships between location of disasters and their physical and social context. / El número de desastres y personas afectadas por esos desastres en el mundo han mostrado una tendencia creciente, especialmente en la segunda mitad del siglo veinte. El impacto físico, económico y social que las amenazas naturales han causado a nivel global ha causado que gobiernos, instituciones internacionales y la academia se interesen cada vez más en los desastres causados por esas amenazas. Este interés ha contribuido a mejorar el conocimiento existente sobre desastres y ha contribuido a multiplicar las iniciativas orientadas a reducir sus efectos negativos en las personas. La escala en la cual las iniciativas para la reducción del riesgo de desastres (RRD) se llevan a cabo es un parámetro crítico para su materialización. Hoy en día se reconoce la estrecha relación que existe entre los desastres y los lugares donde éstos se registran. Por esta razón, estudiar la distribución de los desastres en una escala local es esencial para que la RRD sea práctica y factible para autoridades y organizaciones locales, y también para la sociedad civil. Sin embargo, estudiar los desastres en una escala local es aún un problema por resolver debido a las restricciones impuestas por la escasa disponibilidad de datos de alta resolución. A pesar de las dificultades y limitaciones identificadas, la vulnerabilidad social en las regiones propensas a desastres es un problema importante que necesita ser atendido con prontitud. La presente tesis exploró alternativas metodológicas para estudiar la distribución geográfica de los desastres naturales y sus causas potenciales, particularmente en áreas propensas a desastres y en condiciones de información limitada. La cuenca del Río Samalá fue seleccionada como caso de estudio debido a que es un área representativa de áreas propensa a desastres con alta vulnerabilidad social y además escasez de datos. El trabajo de investigación propone métodos exploratorios para extraer información crítica sobre desastres utilizando la información geográfica y social que esté disponible, evaluando los obstáculos impuestos por la reducida disponibilidad de datos. La información existente fue complementada con el uso de fuentes de información no tradicional, e.g. imágenes satelitales de luces nocturnas, como una manera de superar las limitaciones identificadas. Los métodos desarrollados en este trabajo de tesis tuvieron como objetivos (a) obtener información sobre desastres naturales en condiciones de escasez de datos, (b) explorar la correlación entre la distribución espacial de los desastres naturales y su contexto físico para identificar causalidades, (c) utilizar información de libre acceso para estudiar el contexto social de los desastres como causa potencial de los desastres en áreas con escasez de datos, y (d) mapear vulnerabilidades para sustentar acciones para la RRD. Este trabajo de tesis sostiene que la contribución potencial de los métodos propuestos al desarrollo de la RRD en la escala social es más importante que las incertidumbres que implican y las limitaciones creadas por la reducida calidad y cantidad de información para el caso de estudio. El uso de fuentes de información no tradicionales tales como imágenes satelitales hizo posible incrementar la información sobre las incidencias de desastres y, en particular, buscar relación de dependencia entre los lugares particulares en los que los desastres fueron registrados y su contexto físico y social.

Wetlands are dynamic ecosystems and important sources of natural resources that provide a large array of ecosystem services. Unfortunately, most wetlands are threatened by human and natural stressors, such as damming, irrigation, water abstraction, climate change and variability that compromise the sustainability of the whole system. The Inner Niger Delta (IND), Mali, West Africa, is one of the biggest floodplains in the world, has a vast natural resource that attracts many people to live in and around the delta. The IND is considered a hub of human activities that include agriculture, fishing, transport, and tourism and plays an important role in promoting sustainable development for food security, water management, and the environment. As for most wetlands in the world, the very existence of the IND is at stake with the ever-increasing number of dams and irrigation schemes that are built to feed the growing population in the region. Given the fragility of the system and the multiplicity of water uses in the IND, the current knowledge of the flood dynamics and its relation to ecosystem services and the productivity of economic activity is insufficient. There is no operational hydrodynamic model of the IND, and the Malian authorities rely on simplified models and empirical relations for water resources management in the area. This thesis contributes to a better water resources management of the IND by a) developing the first 2D hydrodynamic model based on a triangular adaptative mesh of the IND that performs well despite the poor quality of available topographic/bathymetric data b) developing an innovative way of accounting for the strong hysteresis phenomenon in the IND in the hydrodynamic modeling that allowed a better reproduction of the hydraulic connectivity between important lakes and the main river and c) developing the first non-stationary relationship between the water levels at a reference station and the flooded area in the IND. The first part of the thesis deals with the challenge of developing a hydrodynamic model using only two low-resolution satellite-derived Digital Elevation Models: the Shuttle Radar Topography Mission (SRTM), which has a 30m horizontal resolution, and the Multi-Error-Removed Improved-Terrain (MERIT). Given the low vertical accuracy of global DEMs, another DEM was derived using the waterline method, by combining water extent map from satellite images and local water level information. Channel depths were approximated using the hydraulic geometric relationship methods, while the friction coefficient was derived from the global land-use class classification (GLCC) data. The river network was extracted from the water extent map corresponding to the lowest water level. Six different hydrodynamic models were created by varying the DEM and downstream boundary conditions. Each of the models was calibrated for discharge and water levels. Bayesian Model Averaging (BMA) was finally used to combine the outputs of all six hydrodynamic models into one robust simulation. In the second part, the effect of hysteresis at the downstream boundary condition of the hydrodynamic model was examined. Existing hydrodynamic models of the IND use a static stage-discharge relationship as a downstream boundary condition during both the rise and recession of the flood, leading to potential inaccuracies in the simulation of the flood extent. This paper explores the improvement in the simulation of the flood and connectivity dynamics resulting from the use of a looped rating curve at the downstream boundary of a hydrodynamic model of the IND. The hysteresis effect is integrated into the rating curve using two methods, one based on dimensionless discharges and levels (DLRC) and the other based on the modified Jones formula (MJRC). Results show that the hysteresis effect is better represented using DLRC and that simulations using any of the modified rating curves improves the accuracy of floodplain extent simulations in the areas close to the downstream station, as well as the timing of the connectivity of the river system to one important lake in the IND. The improvement in water level simulation decreases steadily with distance from the downstream boundary of the modeled area. The third part of the thesis deals with the development of an improved relation between inundation extent and water levels in the IND. Accurate knowledge of the flooded extent considered crucial for the proper management of natural resources in the IND. Several authors have developed empirical relationships between water levels at key stations in the IND and the flooded extent in an attempt to provide simple tools to link hydraulic parameters to the performance socio-economic activities in the IND. However, simulations from a hydrodynamic model of the IND showed that the relationship between water levels and the inundation extents varies greatly from year to year, and cannot be adequately captured by static formulas. First, it is demonstrated in this paper that if the maximum water level area is known in advance, accurate relationships between water levels and inundation extents can be derived. In the second part of the paper, stepwise regression is used to develop a function that can forecast maximum water levels at Akka using observed streamflow and precipitation upstream of the Delta. The combination of the two results allows a realtime estimation of the inundated area in the IND using observed water levels, precipitation, and streamflow.

<p>Flooding is one of the most devastating and frequently occurring natural phenomena in the world. Due to the adverse impacts of floods on the life and property of humans, it is crucial to investigate the best flood modeling approaches for delineation of floodplain areas. Conventionally, different hydrodynamic models are used to identify the floodplain areas. However, the high computational cost, and the dependency of these models on detailed input datasets limit their application for large scale floodplain mapping in data-scarce regions. Recently, a new floodplain mapping method based on a hydrogeomorphic feature, named Height Above Nearest Drainage (<i>HAND</i>), has been proposed as a successful alternative for fast and efficient floodplain mapping at the large scale. The overall goal of this study is to improve the performance of <i>HAND</i>-based method by overcoming its current limitations. The main focus will be on extending the application of the <i>HAND</i>-based method to data-scarce environments. To achieve this goal, regionalization techniques are integrated with the floodplain models at the regional and continental scales. Considering these facts, four research objective are established to (1) Develop a regression model to create 100-year floodplain maps at a regional scale (2) Develop a classification framework for creating 100-year floodplain maps for the Contiguous United States (3) Develop a new version of the <i>HAND</i>-based method for creating probabilistic 100-year floodplain maps, and (4) Propose a general regionalization framework for transferring information from data-rich basins to data-scarce environments. </p> <p> </p> <p>In the first objective, the state of North Carolina is selected as the study area, and a regression model is developed to regionalize the available 100-year Flood Insurance Rate Maps (FIRMs) to the data-scarce regions. The regression model is an exponential equation with three independent variables including the average slope, the average elevation, and the main stream slope of the watershed. The results show that the estimated floodplains are within the expected range of accuracy of C>0.6 and F>0.9 for majority of watersheds located in the mid-altitude regions, but it overpredicts and underpredicts in the flat and mountainous regions respectively. </p> <p> </p> <p>The second objective of this research extends the spatial application of the <i>HAND</i>-based method to the entire United States by proposing a new classification framework. The proposed framework classifies the watersheds into three groups by using seven watershed characteristics related to the topography, climate and land use. The validation results show that the average error of floodplain maps is around 14% which demonstrate the reliability and robustness of the proposed framework for continental floodplain mapping. In addition to the acceptable accuracy, the proposed framework creates the floodplain maps for any watershed within the United States. </p> <p> </p> <p>The <i>HAND</i>-based method is a deterministic modeling approach to floodplain mapping. In the third objective, the probabilistic version of this method is proposed. Using a probabilistic approach to floodplain mapping provides more informative maps. In this study, a flat watershed in the state of Kansas is selected as the case study, and the performance of four probabilistic functions for floodplain mapping is compared. The results show that a linear function with one parameter and a gamma function with two parameters are the best options for this study area. It is also shown that the proposed probabilistic approach can reduce the overpredictions and underpredictions made by the deterministic <i>HAND</i>-based approach. </p> <p> </p> <p>In the fourth objective, a new regionalization framework for transferring the calibrated environmental models to data-scarce regions is proposed. This framework aims to improve the current similarity-based regionalization methods by reducing the subjectivity that exists in the selection of basin descriptors. Using this framework for the probabilistic <i>HAND</i>-based method in the third objective, the floodplains are regionalized for a large set of watersheds in the Central United States. The results show that “vertical component of centroid (or latitude)” is the dominant descriptor of spatial variabilities in the probabilistic floodplain maps. This is an interesting finding which shows how a systematic approach can help to explore the hidden descriptors for regionalization. It is demonstrated that using common methods, such as correlation coefficient calculation, or stepwise regression analysis, will not reveal the critical role of latitude on the spatial variability of floodplains.</p>

Human activities on land have a direct and cumulative impact on water and other natural resources within a catchment. This land-use change can have hydrological consequences on the local and regional scales. Sound catchment assessment is not only critical to understanding processes and functions but also important in identifying priority management areas. The overarching goal of this doctoral thesis was to design a methodological framework for catchment assessment (dependent upon data availability) and propose practical catchment management strategies for sustainable water resources management. The Nyangores and Ruiru reservoir catchments located in Kenya, East Africa were used as case studies. A properly calibrated Soil and Water Assessment Tool (SWAT) hydrologic model coupled with a generic land-use optimization tool (Constrained Multi-Objective Optimization of Land-use Allocation-CoMOLA) was applied to identify and quantify functional trade-offs between environmental sustainability and food production in the ‘data-available’ Nyangores catchment. This was determined using a four-dimension objective function defined as (i) minimizing sediment load, (ii) maximizing stream low flow and (iii and iv) maximizing the crop yields of maize and soybeans, respectively. Additionally, three different optimization scenarios, represented as i.) agroforestry (Scenario 1), ii.) agroforestry + conservation agriculture (Scenario 2) and iii.) conservation agriculture (Scenario 3), were compared. For the data-scarce Ruiru reservoir catchment, alternative methods using digital soil mapping of soil erosion proxies (aggregate stability using Mean Weight Diameter) and spatial-temporal soil loss analysis using empirical models (the Revised Universal Soil Loss Equation-RUSLE) were used. The lack of adequate data necessitated a data-collection phase which implemented the conditional Latin Hypercube Sampling. This sampling technique reduced the need for intensive soil sampling while still capturing spatial variability. The results revealed that for the Nyangores catchment, adoption of both agroforestry and conservation agriculture (Scenario 2) led to the smallest trade-off amongst the different objectives i.e. a 3.6% change in forests combined with 35% change in conservation agriculture resulted in the largest reduction in sediment loads (78%), increased low flow (+14%) and only slightly decreased crop yields (3.8% for both maize and soybeans). Therefore, the advanced use of hydrologic models with optimization tools allows for the simultaneous assessment of different outputs/objectives and is ideal for areas with adequate data to properly calibrate the model. For the Ruiru reservoir catchment, digital soil mapping (DSM) of aggregate stability revealed that susceptibility to erosion exists for cropland (food crops), tea and roadsides, which are mainly located in the eastern part of the catchment, as well as deforested areas on the western side. This validated that with limited soil samples and the use of computing power, machine learning and freely available covariates, DSM can effectively be applied in data-scarce areas. Moreover, uncertainty in the predictions can be incorporated using prediction intervals. The spatial-temporal analysis exhibited that bare land (which has the lowest areal proportion) was the largest contributor to erosion. Two peak soil loss periods corresponding to the two rainy periods of March–May and October–December were identified. Thus, yearly soil erosion risk maps misrepresent the true dimensions of soil loss with averages disguising areas of low and high potential. Also, a small portion of the catchment can be responsible for a large proportion of the total erosion. For both catchments, agroforestry (combining both the use of trees and conservation farming) is the most feasible catchment management strategy (CMS) for solving the major water quantity and quality problems. Finally, the key to thriving catchments aiming at both sustainability and resilience requires urgent collaborative action by all stakeholders. The necessary stakeholders in both Nyangores and Ruiru reservoir catchments must be involved in catchment assessment in order to identify the catchment problems, mitigation strategies/roles and responsibilities while keeping in mind that some risks need to be shared and negotiated, but so will the benefits.:TABLE OF CONTENTS DECLARATION OF CONFORMITY........................................................................ i DECLARATION OF INDEPENDENT WORK AND CONSENT ............................. ii LIST OF PAPERS ................................................................................................. iii ACKNOWLEDGEMENTS ..................................................................................... iv THESIS AT A GLANCE ......................................................................................... v SUMMARY ............................................................................................................ vi List of Figures......................................................................................................... x List of Tables........................................................................................................... x ABBREVIATION..................................................................................................... xi PART A: SYNTHESIS 1. INTRODUCTION ............................................................................................... 1 1.1 Catchment management ...................................................................................1 1.2 Tools to support catchment assessment and management ..............................4 1.3 Catchment management strategies (CMSs)......................................................9 1.4 Concept and research objectives.......................................................................11 2. MATERIAL AND METHODS................................................................................15 2.1. STUDY AREA ..................................................................................................15 2.1.1. Nyangores catchment ...................................................................................15 2.1.2. Ruiru reservoir catchment .............................................................................17 2.2. Using SWAT conceptual model and land-use optimization ..............................19 2.3. Using soil erosion proxies and empirical models ..............................................21 3. RESULTS AND DISCUSSION..............................................................................24 3.1. Assessing multi-metric calibration performance using the SWAT model...........25 3.2. Land-use optimization using SWAT-CoMOLA for the Nyangores catchment. ..26 3.3. Digital soil mapping of soil aggregate stability ..................................................28 3.4. Spatio-temporal analysis using the revised universal soil loss equation (RUSLE) 29 4. CRITICAL ASSESSMENT OF THE METHODS USED ......................................31 4.1. Assessing suitability of data for modelling and overcoming data challenges...31 4.2. Selecting catchment management strategies based on catchment assessment . 35 5. CONCLUSION AND RECOMMENDATIONS ....................................................36 6. REFERENCES ............................ .....................................................................38 PART B: PAPERS PAPER I .................................................................................................................47 PAPER II ................................................................................................................59 PAPER III ...............................................................................................................74 PAPER IV ...............................................................................................................88