Land cover change from national to global scales:A spatiotemporal assessment of trajectories, transitions and drivers

Changes in global land cover (LC) have significant consequences for global environmental change, impacting the sustainability of biogeochemical cycles, ecosystem services, biodiversity, and food security. Different forms of LC change have taken place across the world in recent decades due to a combination of natural and anthropogenic drivers, however, the types of change and rates of change have traditionally been hard to quantify. This thesis exploits the properties of the recently released ESA-CCI-LC product – an internally consistent, high-resolution annual time-series of global LC extending from 1992 to 2018. Specifically, this thesis uses a combination of trajectories and transition maps to quantify LC changes over time at national, continental and global scales, in order to develop a deeper understanding of what, where and when significant changes in LC have taken place and relates these to natural and anthropogenic drivers. This thesis presents three analytical chapters that contribute to achieving the objectives and the overarching aim of the thesis. The first analytical chapter initially focuses on the Nile Delta region of Egypt, one of the most densely populated and rapidly urbanising regions globally, to quantify historic rates of urbanisation across the fertile agricultural land, before modelling a series of alternative futures in which these lands are largely protected from future urban expansion. The results show that 74,600 hectares of fertile agricultural land in the Nile Delta (Old Lands) was lost to urban expansion between 1992 and 2015. Furthermore, a scenario that encouraged urban expansion into the desert and adjacent to areas of existing high population density could be achieved, hence preserving large areas of fertile agricultural land within the Nile Delta. The second analytical chapter goes on to examine LC changes across sub-Saharan Africa (SSA), a complex and diverse environment, through the joint lenses of political regions and ecoregions, differentiating between natural and anthropogenic signals of change and relating to likely drivers. The results reveal key LC change processes at a range of spatial scales, and identify hotspots of LC change. The major five key LC change processes were: (i) “gain of dry forests” covered the largest extent and was distributed across the whole of SSA; (ii) “greening of deserts” found adjacent to desert areas (e.g., the Sahel belt); (iii) “loss of tree-dominated savanna” extending mainly across South-eastern Africa; (iv) “loss of shrub-dominated savanna” stretching across West Africa, and “loss of tropical rainforests” unexpectedly covering the smallest extent, mainly in the DRC, West Africa and Madagascar. The final analytical chapter considers LC change at the global scale, providing a comprehensive assessment of LC gains and losses, trajectories and transitions, including a complete assessment of associated uncertainties. This chapter highlights variability between continents and identifies locations of high LC dynamism, recognising global hotspots for sustainability challenges. At the national scale, the chapter identifies the top 10 countries with the largest percentages of forest loss and urban expansion globally. The results show that the majority of these countries have stabilised their forest losses, however, urban expansion was consistently on the rise in all countries. The thesis concludes with recommendations for future research as global LC products become more refined (spatially, temporally and thematically) allowing deeper insights into the causes and consequences of global LC change to be determined

Land Use Cover Datasets and Validation Tools

This open access book represents a comprehensive review of available land-use cover data and techniques to validate and analyze this type of spatial information. The book provides the basic theory needed to understand the progress of LUCC mapping/modeling validation practice. It makes accessible to any interested user most of the research community's methods and techniques to validate LUC maps and models. Besides, this book is enriched with practical exercises to be applied with QGIS. The book includes a description of relevant global and supra-national LUC datasets currently available. Finally, the book provides the user with all the information required to manage and download these datasets

Investigating uncertainty in global hydrology modelling

As projections of future climate raise concerns over water availability and extreme hydrological events, global hydrology models are increasingly being employed to better understand our global water resources and how they may be affected by climate change. Being a relatively recent development in hydrological science, global hydrology modelling has not yet undergone the same level of assessment and evaluation as catchment scale hydrology modelling. Until now, global hydrology models have presented just one deterministic model output for use in scientific research. Recently, multi-model ensembles have compared these outputs for different global models, but this has been done prematurely as the uncertainties within individual models have yet to be understood. This study demonstrates a rigorous uncertainty investigation of the 123 parameters within the Mac-PDM global hydrology model over 21 global river catchments. Mac-PDM was selected for its relative simplicity amongst global hydrology models, and its suitability for application using high performance computer clusters. A new version of the model, Mac-PDM.14 is provided, with updated soil and vegetation classifications. This model is then subjected to a 100,000 parameter realisation Generalised Likelihood Uncertainty Estimation (GLUE) experiment, requiring 40 days of high performance computing time, and outputting over 2Tb of data. The top performing model parameterisation from this experiment provides an annual average error of 47% when compared to observed records, a 45% improvement over the previous version of the model, Mac-PDM.09. Given the computational expense of such an experiment, smaller sample sizes of parameter realisations are explored. Whilst the top performing parameterisation in a sample size as small as 1,000 can perform almost as well as that from 100,000 parameterisations, the number of good parameterisations is fewer, and the range of model uncertainty may therefore be significantly underestimated. Mac-PDM.14 is shown to have a lower mean absolute relative error than all models involved in both the Water and Global Change (WATCH) project and the Inter-Sectoral Impacts Model Intercomparison Project (ISI-MIP). Parameter uncertainty is compared to model uncertainty, and the uncertainty range between the models within the WATCH and ISI-MIP projects is comparable to the parameter uncertainty within Mac-PDM.14. Catchment specific calibrations of the global hydrology model are explored, and it is demonstrated that the model performance is improved by 22 to 92%, for the Niger and the Yangtze respectively, with catchment specific parameter values over a global calibration. Approximate Bayesian Rejection is applied to explore the catchment specific parameter values that result in good parameter performance. Few trends can be identified from this analysis, which suggests that Mac-PDM may be over-parameterised. Catchment specific calibrations in both high latitude and arid to semi-arid regions show significant improvement over global calibration, which indicate a deficiency in model structure; the addition of a glacier component to Mac-PDM is recommended. Model calibrations are validated using the ISI-MIP forcing dataset, and the best model performance gives an error of 44%. This is a betterment on the performance with the WATCH forcing data used in calibration, and so implies that models not need to be recalibrated every time new forcing datasets are employed. This research highlights that the performance of global hydrology models can be significantly improved by running a parameter uncertainty assessment, and that in catchment scale studies, catchment specific calibration should be carefully considered. Furthermore, the uncertainty within individual global hydrology models is an important consideration that should not be overlooked as these models are increasingly included in ensembles and interdisciplinary studies

Investigating uncertainty in global hydrology modelling

As projections of future climate raise concerns over water availability and extreme hydrological events, global hydrology models are increasingly being employed to better understand our global water resources and how they may be affected by climate change. Being a relatively recent development in hydrological science, global hydrology modelling has not yet undergone the same level of assessment and evaluation as catchment scale hydrology modelling. Until now, global hydrology models have presented just one deterministic model output for use in scientific research. Recently, multi-model ensembles have compared these outputs for different global models, but this has been done prematurely as the uncertainties within individual models have yet to be understood. This study demonstrates a rigorous uncertainty investigation of the 123 parameters within the Mac-PDM global hydrology model over 21 global river catchments. Mac-PDM was selected for its relative simplicity amongst global hydrology models, and its suitability for application using high performance computer clusters. A new version of the model, Mac-PDM.14 is provided, with updated soil and vegetation classifications. This model is then subjected to a 100,000 parameter realisation Generalised Likelihood Uncertainty Estimation (GLUE) experiment, requiring 40 days of high performance computing time, and outputting over 2Tb of data. The top performing model parameterisation from this experiment provides an annual average error of 47% when compared to observed records, a 45% improvement over the previous version of the model, Mac-PDM.09. Given the computational expense of such an experiment, smaller sample sizes of parameter realisations are explored. Whilst the top performing parameterisation in a sample size as small as 1,000 can perform almost as well as that from 100,000 parameterisations, the number of good parameterisations is fewer, and the range of model uncertainty may therefore be significantly underestimated. Mac-PDM.14 is shown to have a lower mean absolute relative error than all models involved in both the Water and Global Change (WATCH) project and the Inter-Sectoral Impacts Model Intercomparison Project (ISI-MIP). Parameter uncertainty is compared to model uncertainty, and the uncertainty range between the models within the WATCH and ISI-MIP projects is comparable to the parameter uncertainty within Mac-PDM.14. Catchment specific calibrations of the global hydrology model are explored, and it is demonstrated that the model performance is improved by 22 to 92%, for the Niger and the Yangtze respectively, with catchment specific parameter values over a global calibration. Approximate Bayesian Rejection is applied to explore the catchment specific parameter values that result in good parameter performance. Few trends can be identified from this analysis, which suggests that Mac-PDM may be over-parameterised. Catchment specific calibrations in both high latitude and arid to semi-arid regions show significant improvement over global calibration, which indicate a deficiency in model structure; the addition of a glacier component to Mac-PDM is recommended. Model calibrations are validated using the ISI-MIP forcing dataset, and the best model performance gives an error of 44%. This is a betterment on the performance with the WATCH forcing data used in calibration, and so implies that models not need to be recalibrated every time new forcing datasets are employed. This research highlights that the performance of global hydrology models can be significantly improved by running a parameter uncertainty assessment, and that in catchment scale studies, catchment specific calibration should be carefully considered. Furthermore, the uncertainty within individual global hydrology models is an important consideration that should not be overlooked as these models are increasingly included in ensembles and interdisciplinary studies