Colombia Mi Pronostico Flood Application: Updating and Improving the Mi Pronostico Flood Web Application to Include an Assessment of Flood Risk

Colombia is a country with highly variable terrain, from the Andes Mountains to plains and coastal areas, many of these areas are prone to flooding disasters. To identify these risk areas NASA's Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used to construct a digital elevation model (DEM) for the study region. The preliminary risk assessment was applied to a pilot study area, the La Mosca River basin. Precipitation data from the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM)'s near-real-time rainfall products as well as precipitation data from the Instituto de Hidrologia, Meteorologia y Estudios Ambientales (the Institute of Hydrology, Meteorology and Environmental Studies, IDEAM) and stations in the La Mosca River Basin were used to create rainfall distribution maps for the region. Using the precipitation data and the ASTER DEM, the web application, Mi Pronstico, run by IDEAM, was updated to include an interactive map which currently allows users to search for a location and view the vulnerability and current weather and flooding conditions. The geospatial information was linked to an early warning system in Mi Pronstico that can alert the public of flood warnings and identify locations of nearby shelters

Carbon storage and floristic dynamics in Peruvian peatland ecosystems

In this thesis I took a novel interdisciplinary approach involving remote sensing, ecological and palaeoecological techniques to address some of the most fundamental gaps in our understanding of Peruvian peatlands. The existence of these peatlands was only recently confirmed and although they were known to store large quantities of carbon, initial assessments of carbon stocks were highly uncertain. In addition, little was known of their biodiversity or how they have developed. Firstly, I used data fusion remote sensing and extensive field data to generate a high resolution, landscape scale map of peatland ecosystems in the largest peatland complex in Amazonia. This approach confirmed that peatland ecosystems in northern Peru are the most carbon dense ecosystems in Amazonia storing up to 1391 ± 710 Mg C ha-1, and have a total carbon stock of 3.14 (0.44–8.15) Pg C, which equates to nearly 50 % of the total above-ground carbon stocks of the whole country. Secondly, I established a new network of floristic inventory plots and described the composition and diversity of peatland tree communities. I demonstrated that peatland pole forest has the lowest alpha diversity of all tree communities in lowland Amazonia. In contrast, by comparing these data with three larger plot networks from other ecosystems in the region, I also showed that they have surprisingly high beta diversity, and harbour important populations of species that were previously thought to be restricted to other habitat types such as white sand forest. Finally, pollen analysis was undertaken across eight peat cores from two sites to test the significance of historical processes in determining current patterns of composition and diversity. Both autogenic (internal biotic) and allogenic (external environmental) processes operating through time were important determinants of current floristic patterns. Demonstrating that such historical processes have an important role in determining the composition of tropical ecosystems is valuable as they are often overlooked – or in many cases impossible to study in such detail. Overall this thesis shows that peatland ecosystems in the Peruvian Amazon have high conservation value both as a carbon store and for regional ecosystem diversity. In addition, peatland ecosystems provide an exciting opportunity to investigate the importance of fundamental historical and ecological processes for determining the composition and diversity of tropical forests

Application of open-access and 3rd party geospatial technology for integrated flood risk management in data sparse regions of developing countries

Floods are one of the most devastating disasters known to man, caused by both natural and anthropogenic factors. The trend of flood events is continuously rising, increasing the exposure of the vulnerable populace in both developed and especially developing regions. Floods occur unexpectedly in some circumstances with little or no warning, and in other cases, aggravate rapidly, thereby leaving little time to plan, respond and recover. As such, hydrological data is needed before, during and after the flooding to ensure effective and integrated flood management. Though hydrological data collection in developed countries has been somewhat well established over long periods, the situation is different in the developing world. Developing regions are plagued with challenges that include inadequate ground monitoring networks attributed to deteriorating infrastructure, organizational deficiencies, lack of technical capacity, location inaccessibility and the huge financial implication of data collection at local and transboundary scales. These limitations, therefore, result in flawed flood management decisions and aggravate exposure of the most vulnerable people. Nigeria, the case study for this thesis, experienced unprecedented flooding in 2012 that led to the displacement of 3,871,53 persons, destruction of infrastructure, disruption of socio-economic activities valued at 16.9 billion US Dollars (1.4% GDP) and sadly the loss of 363 lives. This flood event revealed the weakness in the nation’s flood management system, which has been linked to poor data availability. This flood event motivated this study, which aims to assess these data gaps and explore alternative data sources and approaches, with the hope of improving flood management and decision making upon recurrence. This study adopts an integrated approach that applies open-access geospatial technology to curb data and financial limitations that hinder effective flood management in developing regions, to enhance disaster preparedness, response and recovery where resources are limited. To estimate flood magnitudes and return periods needed for planning purposes, the gaps in hydrological data that contribute to poor estimates and consequently ineffective flood management decisions for the Niger-South River Basin of Nigeria were filled using Radar Altimetry (RA) and Multiple Imputation (MI) approaches. This reduced uncertainty associated with missing data, especially at locations where virtual altimetry stations exist. This study revealed that the size and consistency of the gap within hydrological time series significantly influences the imputation approach to be adopted. Flood estimates derived from data filled using both RA and MI approaches were similar for consecutive gaps (1-3 years) in the time series, while wide (inconsecutive) gaps (> 3 years) caused by gauging station discontinuity and damage benefited the most from the RA infilling approach. The 2012 flood event was also quantified as a 1-in-100year flood, suggesting that if flood management measures had been implemented based on this information, the impact of that event would have been considerably mitigated. Other than gaps within hydrological time series, in other cases hydrological data could be totally unavailable or limited in duration to enable satisfactory estimation of flood magnitudes and return periods, due to finance and logistical limitations in several developing and remote regions. In such cases, Regional Flood Frequency Analysis (RFFA) is recommended, to collate and leverage data from gauging stations in proximity to the area of interest. In this study, RFFA was implemented using the open-access International Centre for Integrated Water Resources Management–Regional Analysis of Frequency Tool (ICI-RAFT), which enables the inclusion of climate variability effect into flood frequency estimation at locations where the assumption of hydrological stationarity is not viable. The Madden-Julian Oscillation was identified as the dominant flood influencing climate mechanism, with its effect increasing with return period. Similar to other studies, climate variability inclusive regional flood estimates were less than those derived from direct techniques at various locations, and higher in others. Also, the maximum historical flood experienced in the region was less than the 1-in-100-year flood event recommended for flood management. The 2012 flood in the Niger-South river basin of Nigeria was recreated in the CAESAR-LISFLOOD hydrodynamic model, combining open-access and third-party Digital Elevation Model (DEM), altimetry, bathymetry, aerial photo and hydrological data. The model was calibrated/validated in three sub-domains against in situ water level, overflight photos, Synthetic Aperture Radar (SAR) (TerraSAR-X, Radarsat2, CosmoSkyMed) and optical (MODIS) satellite images where available, to access model performance for a range of geomorphological and data variability. Improved data availability within constricted river channel areas resulted in better inundation extent and water level reconstruction, with the F-statistic reducing from 0.808 to 0.187 downstream into the vegetation dominating delta where data unavailability is pronounced. Overflight photos helped improve the model to reality capture ratio in the vegetation dominated delta and highlighted the deficiencies in SAR data for delineating flooding in the delta. Furthermore, the 2012 flood was within the confine of a 1-in-100-year flood for the sub-domain with maximum data availability, suggesting that in retrospect the 2012 flood event could have been managed effectively if flood management plans were implemented based on a 1-in-100-year flood. During flooding, fast-paced response is required. However, logistical challenges can hinder access to remote areas to collect the necessary data needed to inform real-time decisions. Thus, this adopts an integrated approach that combines crowd-sourcing and MODIS flood maps for near-real-time monitoring during the peak flood season of 2015. The results highlighted the merits and demerits of both approaches, and demonstrate the need for an integrated approach that leverages the strength of both methods to enhance flood capture at macro and micro scales. Crowd-sourcing also provided an option for demographic and risk perception data collection, which was evaluated against a government risk perception map and revealed the weaknesses in the government flood models caused by sparse/coarse data application and model uncertainty. The C4.5 decision tree algorithm was applied to integrate multiple open-access geospatial data to improve SAR image flood detection efficiency and the outputs were further applied in flood model validation. This approach resulted in F-Statistic improvement from 0.187 to 0.365 and reduced the CAESAR-LISFLOOD model overall bias from 3.432 to 0.699. Coarse data resolution, vegetation density, obsolete/non-existent river bathymetry, wetlands, ponds, uncontrolled dredging and illegal sand mining, were identified as the factors that contribute to flood model and map uncertainties in the delta region, hence the low accuracy depicted, despite the improvements that were achieved. Managing floods requires the coordination of efforts before, during and after flooding to ensure optimal mitigation in the event of an occurrence. In this study, and integrated flood modelling and mapping approach is undertaken, combining multiple open-access data using freely available tools to curb the effects of data and resources deficiency on hydrological, hydrodynamic and inundation mapping processes and outcomes in developing countries. This approach if adopted and implemented on a large-scale would improve flood preparedness, response and recovery in data sparse regions and ensure floods are managed sustainably with limited resources

Degradationsrisiken tropischer Waldökosysteme – Multifaktorielle Fernerkundungs- und GIS-basierte Modellierung der Landschaftsvulnerabilität. Umgesetzt am Fallbeispiel von São Tomé

Die tropischen Ökosysteme sind zunehmend steigenden Risiken durch Landnutzungsdruck ausgesetzt. Für die Quantifizierung und Bewertung der ökologischen Vulnerabilität dieser Ökosysteme fehlen allgemeingültige Konzepte und praktisch anwendbare Modelle. Zudem sind die tropischen Waldökosysteme Afrikas wenig erforscht. Im Rahmen dieser Arbeit erfolgt eine konzeptionelle Entwicklung eines räumlich hochauflösenden, multifaktoriellen Landschaftsvulnerabilitätsmodells als Ausdruck für die ökologische Vulnerabilität tropischer Ökosysteme. Das Modell der Landschaftsvulnerabilität (LV = Anfälligkeit der Landschaft für anthropogene Gefährdungen) wird am Fallbeispiel des tropischen Inselökosystems von São Tomé umgesetzt. Die international kaum bekannte Insel São Tomé (859 km²) liegt im Atlantik vor der Westküste des tropischen Zentralafrikas. Aufgrund des Status als Hotspot der Biodiversität mit vielen endemischen Arten sowie großer Landschaftsästhetik besitzt São Tomé einen hohen ökologischen Wert. Die Gesamtfläche des Primär- bzw. Altwaldes und des Sekundärwaldes beläuft sich auf ca. 50 %. Hinsichtlich einer schnell ansteigenden Einwohnerzahl auf São Tomé erhöht sich kontinuierlich der Landnutzungsdruck in Form von Walddegradation und Biodiversitätsgefährdung. Die methodischen Grundlagen der Forschungsarbeit basieren auf einem integrierten GIS- (Analyse bzw. Modellierung der LV) und Fernerkundungs-Konzept (LULC-Klassifikation). Das LV-Modell, gekennzeichnet durch eine linear-hierarchische Struktur, stützt sich auf bodenkundliche, topographische, fernerkundungsbasierte, statistische und infrastrukturelle Ausgangsdaten. Die Bewertungsanalyse erfolgt multifaktoriell mit einer anschließenden räumlichen Überlagerungsanalyse und gewichteter Summe. Die Ergebnisse sind nach der Intensitätsklassifizierung der LV räumlich-differenziert und geben Auskunft über die Intensität der Vulnerabilität in verschiedenen Landschaftsbereichen. Dadurch können Landschaftsabschnitte identifiziert werden, die für potentielle anthropogen verursachte Gefährdungen anfällig sind. Die gewonnene Information kann das Landmanagement optimieren und zum Biodiversitätsschutz auf São Tomé beitragen. Dank des exemplarischen Ansatzes ist dieses Konzept auch auf andere regional und klimatisch ähnliche tropische Systeme übertragbar. Darüber hinaus können die aus dem Modellansatz gewonnenen Erkenntnisse für die Bewertung der Vulnerabilität tropischer Ökosysteme auch zur Disaster Risk Reduction (DRR) beitragen