NOVEL METHODS FOR OCCUPATIONAL AND NON-OCCUPATIONAL EXPOSURE ASSESSMENT FOR IMPROVED RISK ASSESSMENT AND DECISION MAKING

Human health risk assessments require accurate exposure assessments to be meaningful. Answering, or improving current answers, to exposure questions can require novel methodologies that provide additional utility to existing approaches. We demonstrate three such methods, one for data collection and two for data analysis. While the methods were developed specifically for this dissertation, they are scalable to other applications and can be generalized to similar research questions. 1) In a situation where there is a nearly 30-year gap in data collection, estimate benzene exposure and cancer risk to non-occupational and occupational groups from commercial gasoline station filling operations. 2) With an existing low-cost sensor network measuring ambient PM2.5, utilize probabilistic machine learning models to improve on predictive accuracy of previously developed linear models and then use the output to conduct probabilistic exposure assessments. 3) Create a probabilistic machine learning calibration model for CO sensors deployed in an occupational low-cost sensor network and use the models to create probabilistic concentration hazard maps and American Industrial Hygiene Association exposure category hazard maps to assist with decision making

Re-evaluating participatory catchment management: Integrating mapping, modelling, and participatory action to deliver more effective risk management

Recent policy changes, such as the EU Water Framework Directive, have transformed catchment management to consider connected socio-ecological systems at the catchment scale, and integrate concept of public participation. However, there is relatively little research exploring how effective these changes have been in altering existing practices of management. Adopting a transdisciplinary approach, this thesis investigates a range of perspectives to explore existing participatory practices in current catchment management, and understand how we can integrate alternative knowledges and perspectives. The research employs diverse social and physical science methods, including participant led interviews and participatory mapping, numerical flood modelling, and the creation of a participatory competency group. The research finds that, despite the participatory policy turn, established supracatchment scale drivers continue to dictate top-down practices of everyday catchment management, excluding local communities from decision-making power. In contrast, participation in managing extreme events is actively encouraged, with the development of community resilience a key objective for management agencies. However, the research findings suggest that a similar lack of meaningful participation in knowledge creation and decision-making restricts resilience building. Based on these findings, the research explores practical ways in which participation and resilience can be embedded in ICM, using the typically expert-led practice of numerical flood modelling to show how existing practices of knowledge creation can be enhanced. The thesis also demonstrates how new practices of knowledge creation, based on social learning, can be used to develop new, more effective ways of communicating flood risk and building local resilience. The thesis proposes a new framework for the management of connected socio-ecological catchment systems, embedding evolutionary resilience as a practical mechanism by which public participation and the management of everyday and extreme events could be unified to develop more effective and sustainable catchment management and more resilient communities

An enactive approach to perceptual augmentation in mobility

Event predictions are an important constituent of situation awareness, which is a key objective for many applications in human-machine interaction, in particular in driver assistance. This work focuses on facilitating event predictions in dynamic environments. Its primary contributions are 1) the theoretical development of an approach for enabling people to expand their sampling and understanding of spatiotemporal information, 2) the introduction of exemplary systems that are guided by this approach, 3) the empirical investigation of effects functional prototypes of these systems have on human behavior and safety in a range of simulated road traffic scenarios, and 4) a connection of the investigated approach to work on cooperative human-machine systems. More specific contents of this work are summarized as follows: The first part introduces several challenges for the formation of situation awareness as a requirement for safe traffic participation. It reviews existing work on these challenges in the domain of driver assistance, resulting in an identification of the need to better inform drivers about dynamically changing aspects of a scene, including event probabilities, spatial and temporal distances, as well as a suggestion to expand the scope of assistance systems to start informing drivers about relevant scene elements at an early stage. Novel forms of assistance can be guided by different fundamental approaches that target either replacement, distribution, or augmentation of driver competencies. A subsequent differentiation of these approaches concludes that an augmentation-guided paradigm, characterized by an integration of machine capabilities into human feedback loops, can be advantageous for tasks that rely on active user engagement, the preservation of awareness and competence, and the minimization of complexity in human- machine interaction. Consequently, findings and theories about human sensorimotor processes are connected to develop an enactive approach that is consistent with an augmentation perspective on human-machine interaction. The approach is characterized by enabling drivers to exercise new sensorimotor processes through which safety-relevant spatiotemporal information may be sampled. In the second part of this work, a concept and functional prototype for augmenting the perception of traffic dynamics is introduced as a first example for applying principles of this enactive approach. As a loose expression of functional biomimicry, the prototype utilizes a tactile inter- face that communicates temporal distances to potential hazards continuously through stimulus intensity. In a driving simulator study, participants quickly gained an intuitive understanding of the assistance without instructions and demonstrated higher driving safety in safety-critical highway scenarios. But this study also raised new questions such as whether benefits are due to a continuous time-intensity encoding and whether utility generalizes to intersection scenarios or highway driving with low criticality events. Effects of an expanded assistance prototype with lane-independent risk assessment and an option for binary signaling were thus investigated in a separate driving simulator study. Subjective responses confirmed quick signal understanding and a perception of spatial and temporal stimulus characteristics. Surprisingly, even for a binary assistance variant with a constant intensity level, participants reported perceiving a danger-dependent variation in stimulus intensity. They further felt supported by the system in the driving task, especially in difficult situations. But in contrast to the first study, this support was not expressed by changes in driving safety, suggesting that perceptual demands of the low criticality scenarios could be satisfied by existing driver capabilities. But what happens if such basic capabilities are impaired, e.g., due to poor visibility conditions or other situations that introduce perceptual uncertainty? In a third driving simulator study, the driver assistance was employed specifically in such ambiguous situations and produced substantial safety advantages over unassisted driving. Additionally, an assistance variant that adds an encoding of spatial uncertainty was investigated in these scenarios. Participants had no difficulties to understand and utilize this added signal dimension to improve safety. Despite being inherently less informative than spatially precise signals, users rated uncertainty-encoding signals as equally useful and satisfying. This appreciation for transparency of variable assistance reliability is a promising indicator for the feasibility of an adaptive trust calibration in human-machine interaction and marks one step towards a closer integration of driver and vehicle capabilities. A complementary step on the driver side would be to increase transparency about the driver’s mental states and thus allow for mutual adaptation. The final part of this work discusses how such prerequisites of cooperation may be achieved by monitoring mental state correlates observable in human behavior, especially in eye movements. Furthermore, the outlook for an addition of cooperative features also raises new questions about the bounds of identity as well as practical consequences of human-machine systems in which co-adapting agents may exercise sensorimotor processes through one another.Die Vorhersage von Ereignissen ist ein Bestandteil des Situationsbewusstseins, dessen Unterstützung ein wesentliches Ziel diverser Anwendungen im Bereich Mensch-Maschine Interaktion ist, insbesondere in der Fahrerassistenz. Diese Arbeit zeigt Möglichkeiten auf, Menschen bei Vorhersagen in dynamischen Situationen im Straßenverkehr zu unterstützen. Zentrale Beiträge der Arbeit sind 1) eine theoretische Auseinandersetzung mit der Aufgabe, die menschliche Wahrnehmung und das Verständnis von raum-zeitlichen Informationen im Straßenverkehr zu erweitern, 2) die Einführung beispielhafter Systeme, die aus dieser Betrachtung hervorgehen, 3) die empirische Untersuchung der Auswirkungen dieser Systeme auf das Nutzerverhalten und die Fahrsicherheit in simulierten Verkehrssituationen und 4) die Verknüpfung der untersuchten Ansätze mit Arbeiten an kooperativen Mensch-Maschine Systemen. Die Arbeit ist in drei Teile gegliedert: Der erste Teil stellt einige Herausforderungen bei der Bildung von Situationsbewusstsein vor, welches für die sichere Teilnahme am Straßenverkehr notwendig ist. Aus einem Vergleich dieses Überblicks mit früheren Arbeiten zeigt sich, dass eine Notwendigkeit besteht, Fahrer besser über dynamische Aspekte von Fahrsituationen zu informieren. Dies umfasst unter anderem Ereigniswahrscheinlichkeiten, räumliche und zeitliche Distanzen, sowie eine frühere Signalisierung relevanter Elemente in der Umgebung. Neue Formen der Assistenz können sich an verschiedenen grundlegenden Ansätzen der Mensch-Maschine Interaktion orientieren, die entweder auf einen Ersatz, eine Verteilung oder eine Erweiterung von Fahrerkompetenzen abzielen. Die Differenzierung dieser Ansätze legt den Schluss nahe, dass ein von Kompetenzerweiterung geleiteter Ansatz für die Bewältigung jener Aufgaben von Vorteil ist, bei denen aktiver Nutzereinsatz, die Erhaltung bestehender Kompetenzen und Situationsbewusstsein gefordert sind. Im Anschluss werden Erkenntnisse und Theorien über menschliche sensomotorische Prozesse verknüpft, um einen enaktiven Ansatz der Mensch-Maschine Interaktion zu entwickeln, der einer erweiterungsgeleiteten Perspektive Rechnung trägt. Dieser Ansatz soll es Fahrern ermöglichen, sicherheitsrelevante raum-zeitliche Informationen über neue sensomotorische Prozesse zu erfassen. Im zweiten Teil der Arbeit wird ein Konzept und funktioneller Prototyp zur Erweiterung der Wahrnehmung von Verkehrsdynamik als ein erstes Beispiel zur Anwendung der Prinzipien dieses enaktiven Ansatzes vorgestellt. Dieser Prototyp nutzt vibrotaktile Aktuatoren zur Kommunikation von Richtungen und zeitlichen Distanzen zu möglichen Gefahrenquellen über die Aktuatorposition und -intensität. Teilnehmer einer Fahrsimulationsstudie waren in der Lage, in kurzer Zeit ein intuitives Verständnis dieser Assistenz zu entwickeln, ohne vorher über die Funktionalität unterrichtet worden zu sein. Sie zeigten zudem ein erhöhtes Maß an Fahrsicherheit in kritischen Verkehrssituationen. Doch diese Studie wirft auch neue Fragen auf, beispielsweise, ob der Sicherheitsgewinn auf kontinuierliche Distanzkodierung zurückzuführen ist und ob ein Nutzen auch in weiteren Szenarien vorliegen würde, etwa bei Kreuzungen und weniger kritischem longitudinalen Verkehr. Um diesen Fragen nachzugehen, wurden Effekte eines erweiterten Prototypen mit spurunabhängiger Kollisionsprädiktion, sowie einer Option zur binären Kommunikation möglicher Kollisionsrichtungen in einer weiteren Fahrsimulatorstudie untersucht. Auch in dieser Studie bestätigen die subjektiven Bewertungen ein schnelles Verständnis der Signale und eine Wahrnehmung räumlicher und zeitlicher Signalkomponenten. Überraschenderweise berichteten Teilnehmer größtenteils auch nach der Nutzung einer binären Assistenzvariante, dass sie eine gefahrabhängige Variation in der Intensität von taktilen Stimuli wahrgenommen hätten. Die Teilnehmer fühlten sich mit beiden Varianten in der Fahraufgabe unterstützt, besonders in Situationen, die von ihnen als kritisch eingeschätzt wurden. Im Gegensatz zur ersten Studie hat sich diese gefühlte Unterstützung nur geringfügig in einer messbaren Sicherheitsveränderung widergespiegelt. Dieses Ergebnis deutet darauf hin, dass die Wahrnehmungsanforderungen der Szenarien mit geringer Kritikalität mit den vorhandenen Fahrerkapazitäten erfüllt werden konnten. Doch was passiert, wenn diese Fähigkeiten eingeschränkt werden, beispielsweise durch schlechte Sichtbedingungen oder Situationen mit erhöhter Ambiguität? In einer dritten Fahrsimulatorstudie wurde das Assistenzsystem in speziell solchen Situationen eingesetzt, was zu substantiellen Sicherheitsvorteilen gegenüber unassistiertem Fahren geführt hat. Zusätzlich zu der vorher eingeführten Form wurde eine neue Variante des Prototyps untersucht, welche räumliche Unsicherheiten der Fahrzeugwahrnehmung in taktilen Signalen kodiert. Studienteilnehmer hatten keine Schwierigkeiten, diese zusätzliche Signaldimension zu verstehen und die Information zur Verbesserung der Fahrsicherheit zu nutzen. Obwohl sie inherent weniger informativ sind als räumlich präzise Signale, bewerteten die Teilnehmer die Signale, die die Unsicherheit übermitteln, als ebenso nützlich und zufriedenstellend. Solch eine Wertschätzung für die Transparenz variabler Informationsreliabilität ist ein vielversprechendes Indiz für die Möglichkeit einer adaptiven Vertrauenskalibrierung in der Mensch-Maschine Interaktion. Dies ist ein Schritt hin zur einer engeren Integration der Fähigkeiten von Fahrer und Fahrzeug. Ein komplementärer Schritt wäre eine Erweiterung der Transparenz mentaler Zustände des Fahrers, wodurch eine wechselseitige Anpassung von Mensch und Maschine möglich wäre. Der letzte Teil dieser Arbeit diskutiert, wie diese Transparenz und weitere Voraussetzungen von Mensch-Maschine Kooperation erfüllt werden könnten, indem etwa Korrelate mentaler Zustände, insbesondere über das Blickverhalten, überwacht werden. Des Weiteren ergeben sich mit Blick auf zusätzliche kooperative Fähigkeiten neue Fragen über die Definition von Identität, sowie über die praktischen Konsequenzen von Mensch-Maschine Systemen, in denen ko-adaptive Agenten sensomotorische Prozesse vermittels einander ausüben können

Urban air pollution modelling with machine learning using fixed and mobile sensors

Detailed air quality (AQ) information is crucial for sustainable urban management, and many regions in the world have built static AQ monitoring networks to provide AQ information. However, they can only monitor the region-level AQ conditions or sparse point-based air pollutant measurements, but cannot capture the urban dynamics with high-resolution spatio-temporal variations over the region. Without pollution details, citizens will not be able to make fully informed decisions when choosing their everyday outdoor routes or activities, and policy-makers can only make macroscopic regulating decisions on controlling pollution triggering factors and emission sources. An increasing research effort has been paid on mobile and ubiquitous sampling campaigns as they are deemed the more economically and operationally feasible methods to collect urban AQ data with high spatio-temporal resolution. The current research proposes a Machine Learning based AQ Inference (Deep AQ) framework from data-driven perspective, consisting of data pre-processing, feature extraction and transformation, and pixelwise (grid-level) AQ inference. The Deep AQ framework is adaptable to integrate AQ measurements from the fixed monitoring sites (temporally dense but spatially sparse), and mobile low-cost sensors (temporally sparse but spatially dense). While instantaneous pollutant concentration varies in the micro-environment, this research samples representative values in each grid-cell-unit and achieves AQ inference at 1 km \times 1 km pixelwise scale. This research explores the predictive power of the Deep AQ framework based on samples from only 40 fixed monitoring sites in Chengdu, China (4,900 {\mathrm{km}}^\mathrm{2}, 26 April - 12 June 2019) and collaborative sampling from 28 fixed monitoring sites and 15 low-cost sensors equipped with taxis deployed in Beijing, China (3,025 {\mathrm{km}}^\mathrm{2}, 19 June - 16 July 2018). The proposed Deep AQ framework is capable of producing high-resolution (1 km \times 1 km, hourly) pixelwise AQ inference based on multi-source AQ samples (fixed or mobile) and urban features (land use, population, traffic, and meteorological information, etc.). This research has achieved high-resolution (1 km \times 1 km, hourly) AQ inference (Chengdu: less than 1% spatio-temporal coverage; Beijing: less than 5% spatio-temporal coverage) with reasonable and satisfactory accuracy by the proposed methods in urban cases (Chengdu: SMAPE \mathrm{<} 20%; Beijing: SMAPE \mathrm{<} 15%). Detailed outcomes and main conclusions are provided in this thesis on the aspects of fixed and mobile sensing, spatio-temporal coverage and density, and the relative importance of urban features. Outcomes from this research facilitate to provide a scientific and detailed health impact assessment framework for exposure analysis and inform policy-makers with data driven evidence for sustainable urban management.Open Acces

Quantifying the spatio-temporal temperature dynamics of Greater London using thermal Earth observation

PhD ThesisUrban areas are highly sensitive to extreme events such as heatwaves. In order to understand how cities will respond to thermal stress it is critical to quantify not only their temporal temperature dynamics but also their spatial temperature variability. However, many cities lack weather station networks with a sufficient spatial distribution to characterise spatio-temporal intraurban temperature dynamics. One means by which spatially complete measurements of urban temperature may be derived is to employ satellite thermal Earth observed data. While some success has been achieved in understanding the temperature characteristics of cities using such data, relatively little work has been undertaken on establishing the use of long time-series Earth observed data as a supplement or alternative to screen-level air temperatures frequently utilised in urban climatological studies. In this thesis a software framework, centred around the use of a spatial database, is developed which can be used to gain an improved understanding of how satellite thermal Earth observed data can be used in the long timeseries analysis of urban temperature dynamics. The utility of the system is demonstrated by processing a 23 year time series (1985-2008) of 1,141 Advanced Very High Resolution Radiometer (AVHRR) images and hourly United Kingdom (UK) Met Office weather station measurements for the Greater London area. London was selected as the region of interest as it is the UK’s only megacity, and has been shown to exhibit both a significant urban heat island and a severe increase in population mortality during previous heatwave events. The software framework was employed to conduct two inter-related sets of analysis. First, the relationship over time between AVHRR estimated surface temperature (EST) and screen-level air temperature records is investigated and quantified. The resulting relationships are then used to produce an empirical model that can predict spatially complete summer-season air temperi atures for London. Cross-validation testing of the model at selected London weather stations showed model root mean square error (RMSE) ranging from 2.70 to 2.94°C and absolute errors in air temperature estimation of 0.45 to 1.67°C. A key finding of the thesis is that the minimal variation in prediction error between the different stations indicate a level of spatial robustness in the model across the urban surface, that is within the limits of the AVHRR EST precision. In addition, the model was used to estimate spatially averaged air temperatures over the Greater London area for selected summers, and showed a maximum error in air temperature prediction of 1.44°C. Furthermore, the prediction error for the heatwave summer of 2003 was 0.51°C, suggesting that such a model can successfully be used to estimate air temperatures for extreme heatwave summers. Such predictions are directly relevant to future assessments of urban population exposure to heatwaves, and it is envisaged that they could be used in conjunction with a population vulnerability index to create a spatially complete heatwave risk map for London. This work is then extended to investigate the utility of satellite estimated surface temperature measurements to characterise temporally and spatially intra-urban heatwave dynamics using the commonly employed urban heat island intensity metric (UHII). Analysis of the AVHRR EST found that the data are highly sensitive to local meteorological conditions, and that temporal aggregation at the monthly scale is required to provide robust data-sets for inter-year analysis of summer temperatures and generation of the UHII metric. Statistical testing of EST and air-temperature derived UHII for the heatwave summer of 2003 against other non-heatwave summers showed no significant increase in intensity at the 95% confidence level. This raises questions as to the applicability of the UHII metric to capture increases in urban temperatures during a heatwave event.Engineering and Physical Sciences Research Council and the School of Civil Engineering and Geoscience

Fusion of Data from Heterogeneous Sensors with Distributed Fields of View and Situation Evaluation for Advanced Driver Assistance Systems

In order to develop a driver assistance system for pedestrian protection, pedestrians in the environment of a truck are detected by radars and a camera and are tracked across distributed fields of view using a Joint Integrated Probabilistic Data Association filter. A robust approach for prediction of the system vehicles trajectory is presented. It serves the computation of a probabilistic collision risk based on reachable sets where different sources of uncertainty are taken into account

Interactive Planning and Sensing for Aircraft in Uncertain Environments with Spatiotemporally Evolving Threats

Autonomous aerial, terrestrial, and marine vehicles provide a platform for several applications including cargo transport, information gathering, surveillance, reconnaissance, and search-and-rescue. To enable such applications, two main technical problems are commonly addressed.On the one hand, the motion-planning problem addresses optimal motion to a destination: an application example is the delivery of a package in the shortest time with least fuel. Solutions to this problem often assume that all relevant information about the environment is available, possibly with some uncertainty. On the other hand, the information gathering problem addresses the maximization of some metric of information about the environment: application examples include such as surveillance and environmental monitoring. Solutions to the motion-planning problem in vehicular autonomy assume that information about the environment is available from three sources: (1) the vehicle’s own onboard sensors, (2) stationary sensor installations (e.g. ground radar stations), and (3) other information gathering vehicles, i.e., mobile sensors, especially with the recent emphasis on collaborative teams of autonomous vehicles with heterogeneous capabilities. Each source typically processes the raw sensor data via estimation algorithms. These estimates are then available to a decision making system such as a motion- planning algorithm. The motion-planner may use some or all of the estimates provided. There is an underlying assumption of “separation� between the motion-planning algorithm and the information about environment. This separation is common in linear feedback control systems, where estimation algorithms are designed independent of control laws, and control laws are designed with the assumption that the estimated state is the true state. In the case of motion-planning, there is no reason to believe that such a separation between the motion-planning algorithm and the sources of estimated environment information will lead to optimal motion plans, even if the motion planner and the estimators are themselves optimal. The goal of this dissertation is to investigate whether the removal of this separation, via interactive motion-planning and sensing, can significantly improve the optimality of motion- planning. The major contribution of this work is interactive planning and sensing. We consider the problem of planning the path of a vehicle, which we refer to as the actor, to traverse a threat field with minimum threat exposure. The threat field is an unknown, time- variant, and strictly positive scalar field defined on a compact 2D spatial domain – the actor’s workspace. The threat field is estimated by a network of mobile sensors that can measure the threat field pointwise. All measurements are noisy. The objective is to determine a path for the actor to reach a desired goal with minimum risk, which is a measure sensitive not only to the threat exposure itself, but also to the uncertainty therein. A novelty of this problem setup is that the actor can communicate with the sensor network and request that the sensors position themselves in a procedure we call sensor reconfiguration such that the actor’s risk is minimized. This work continues with a foundation in motion planning in time-varying fields where waiting is a control input. Waiting is examined in the context of finding an optimal path with considerations for the cost of exposure to a threat field, the cost of movement, and the cost of waiting. For example, an application where waiting may be beneficial in motion-planning is the delivery of a package where adverse weather may pose a risk to the safety of a UAV and its cargo. In such scenarios, an optimal plan may include “waiting until the storm passes.� Results on computational efficiency and optimality of considering waiting in path- planning algorithms are presented. In addition, the relationship of waiting in a time- varying field represented with varying levels of resolution, or multiresolution is studied. Interactive planning and sensing is further developed for the case of time-varying environments. This proposed extension allows for the evaluation of different mission windows, finite sensor network reconfiguration durations, finite planning durations, and varying number of available sensors. Finally, the proposed method considers the effect of waiting in the path planner under the interactive planning and sensing for time-varying fields framework. Future work considers various extensions of the proposed interactive planning and sensing framework including: generalizing the environment using Gaussian processes, sensor reconfiguration costs, multiresolution implementations, nonlinear parameters, decentralized sensor networks and an application to aerial payload delivery by parafoil

Flood dynamics, surface water retention and availability in the semiarid Cuvelai-Basin, southern Angola and northern Namibia

Located in the western part of the Cuvelai Basin, the Iishana system is a transboundary region covering parts of southern Angola and northern Namibia. Hydrologically, this region is characterized by a network of episodically water-bearing channels in which numerous pans are embedded. These pans, which fill up during the rainy season, form an important water resource for the rural population, especially for agricultural and domestic use. The Iishana system is one of the most densely populated areas in southwestern Africa, and this high population trend is increasing (NamStat 2013). To date, the majority of the population (80–90%) currently lives in rural areas. However, (small) cities are experiencing steady growth. The semi-arid climate in this area has distinct rainy and dry seasons and is characterized by high interannual variability, resulting both in intense droughts and in strong flood events. As a result, water is sometimes a scarce resource in this region. The strong population growth and the temperature increase predicted as a result of global climate change will put further pressure on available water resources. However, as this region is also subject to volatile rainfall dynamics, in addition to droughts, the Iishana system also experiences repeated, severe flood events. Most recently, flood events occurred in 2008 to 2011, 2013, and 2017, resulting in the loss of life, the loss of crop yields and consequent loss of livelihood for many people, and the destruction of key infrastructure elements. To date, there has been no complex 2D-hydrodynamic model for the Iishana system and no transferable modeling approach to identify potential locations for water storage and facilitate the planning and development of flood retention measures. In this study, various methods have been developed and applied to address these issues. This has allowed for the validation of existing findings as well as the discovery of new insights, which are briefly summarized below. First, an investigation was performed to test the influence of topography on hydrology, with a special emphasis on infrastructure elements. The focus here was on improving the raw DEM for subsequent calculations. For this purpose, filter corrections were performed on the TanDEM X raw data, and road dams, culverts, and bridges were recorded by means of kinematic surveys. As a result, the definition of the flow paths was improved. It became clear that northern roads, especially those running from east to west, have a strong influence on the runoff behavior in the study area due to their height and their orientation orthogonal to the water flow of the Iishana. Based on the corrected DEM and the application of a modified Blue Spot Analysis, further new findings emerged. Approximately 190,000 pans with a total storage volume of about 1.9 km³ and a total area of 4,021 km² were identified. The part of the study area located in Angola accounts for two thirds of the potential storage volume while only one third of the storage volume is in Namibia. Furthermore, about one third of all pans are located in the episodically water-bearing channels. Based on previous results in other regions, a calculation of the surface-volume relationship (SA/V rate) was performed for the first time for the Iishana system. This enabled the identification of about 2,000 pans that are primarily suitable for an expansion of storage volume. Using continuous and spatio-temporally varying TRMM precipitation data, a 2D-hydrodynamic modeling and reconstruction of the 2008/2009 flood event was performed using the FloodArea model. Although the results represent a snapshot, they nevertheless contribute to an improved understanding of the interconnected runoff system and highlight potential flood hazards. Depending on the weighting of evapotranspiration in the calculation of the model, the potential storage volume can be quantified between 0.116 km³ and 0.547 km³. The total inundation area was calculated at 1.860 km². In addition, three main runoff paths were identified, of which the central and the eastern runoff paths pose a particular threat to the regional capital of Oshakati. Furthermore, with the help of the model, for the first time it was possible to identify areas where, after the end of the rainy season, water availability is naturally shortest (Namibia) or longest (Angola). Based on these numerous, new results, scenario calculations for neighboring catchments as well as calculations for other precipitation periods can be performed in the future. Thus, the duration of water availability after the end of a rainy season can be determined and possible locations for retention measures can be identified for various locations