Search Results for: item/10864860/PhD-position-Global-flood-risk-assessment-and-behavioural-modelling-VU-Amsterdam/page/49/www.linkedin.com/in/apoorva-seeram

Climate change and land use change are two major driving forces of global change but are rarely considered jointly. Their synergistic impacts have large impacts on humans and environment. This research looks at the needs that arise from these impacts, assesses whether current climate funding is in line with those needs, proposes a framework for improved investment decision-making, and provides a case study of a landscape in West-Africa. There will be a focus on Sub-Saharan Africa, a region where land use change is common, climate change impacts are expected to high, and for which insufficient funding is available.

Lakes are of vital importance to the Chinese population for example by providing a source of drinking water. Taihu Lake is iconic in this perspective by being an important water source for its surrounding cities. The lake experiences frequent reoccurring algal blooms since 2007, threatening the drinking water safety of tens of millions of people. Therefore, it is valuable to study the spatial and temporal distribution of algal bloom and analyse which factors impact algal blooms in Taihu Lake to support the prediction of algal blooms. In traditional studies of algal blooms, there are limitations in data size and data analytical methods. This project will use big data and deep learning technologies to obtain more accurate algal distribution data in Taihu Lake, analyze its spatial and temporal distribution, identify key influencing factors, and apply this data and information into agal bloom data-driven models.

Emissions of ammonia to air from the agricultural sector have large negative societal impacts. Ammonia contributes to eutrophication and acidification of terrestrial and marine ecosystems and loss of biodiversity. Despite its central role in many environmental threats, the uncertainty in agricultural ammonia emissions is large. Recently, the need of temporal explicit emission information was highlighted as the fate of air pollutants after emission is highly dependent on concurring meteorological conditions. We aim to improve the quantification of agricultural ammonia emissions at European scale using earth observation data. Remote sensed data will be used to increase insight in the temporal dynamics of agricultural practices and emissions over the year by adding spatial and temporal resolution, not available through statistical agencies. The data from various satellites provide important variables, such as crop distributions, biomass density, soil moisture, snow cover and surface temperature. The satellites provide direct global observations, as opposed to point measurements. The inherent spatial variability of the observed variables will be reflected in the derived emission distributions. The atmospheric ammonia budget will be modelled using LOTOS-EUROS. We aim to verify the novel emission product using ammonia column data from IASI and CrIS as well as in-situ observations. Finally, we aim to invert ammonia emission strengths from the IASI data with LOTOS-EUROS. The contribution of the agricultural sector to nitrogen deposition in Europe will thus be quantified, being a crucial product in view of biodiversity impacts.

This study aims to understand the governing processes and quantify sediment connectivity at catchment and reach scales, and relation with riverbank erosion that affect morphodynamics in semi-arid river systems. As the morphodynamics of the catchment and river network system of reach levels are complicated system and demanding high input data, this study will apply reduced complexity approaches of GIS, connectivity index , and simplified models (e.g, CASCADE, CAESAR-Lisflood) using satellite images, archives and global available datasets, and flume experiment will apply to test the relation between sediment connectivity and bank erosion in response to key drivers.

Climate change impact analysis is important for the national climate change adaptation plan in Indonesia. This is particularly true for changes in the rainy season and extremes, which impact on the food security and hydro-meteorological disaster in Indonesia. It is still unclear how the seasonal and extreme rainfall change will change in the future in a high resolution perspective and how it will impact the future agriculture production and flooding condition. In the EU-funded Primavera project an ensemble of very high-resolution global climate model simulations (HighResMIP) for the climate up to 2050 is produced. These data are analysed to assess climate change impacts in Indonesia. In this research, we aim to bridge the gap between climate change projection result and policy makers in agriculture and disaster management sectors by post-processing the high-resolution climate projection datasets to actionable information for the national climate change adaptation plan.