Reconstruction of multiple climate variables at high spatiotemporal resolution based on Big Earth data platform
- Publication Type:
- Thesis
- Issue Date:
- 2021
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Reconstruction of climate variables with high spatio-temporal resolution is important when the meteorological observations required for environmental monitoring and modelling do not cover the study area. In addition, climate model reanalysis datasets suffer from coarse spatio-temporal resolutions, which fails to capture the complex variability of climate at fine scales. This thesis mainly reconstructed four climate datasets including: mountainous solar radiation, near-surface air temperature datasets over rugged terrain, five distinct metrics of long-term heat wave datasets, an updated database of water and wind erosion. For further use in practice, these datasets are freely accessible and online web application has been developed for academic research on climate change under accelerated global warming. The main findings of this thesis are: (1) A GIS‐based solar radiation model that incorporates albedo, shading by surrounding terrain, and variations in cloudiness was developed to address the spatial variability of these factors in mountainous terrain. (2) The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high‐quality near‐surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we create new near-surface air temperature datasets to understand elevation-dependent warming in the Tibetan Plateau. (3) Under ongoing global warming due to climate change, heat waves in Australia are expected to become more frequent and severe. A Google Earth Engine-based toolkit named heat wave tracker (HWT) is developed, which can be used for dynamic visualization, extraction, and processing of complex heat wave events. The datasets, toolkit, and findings we developed contribute to global studies on heat waves under accelerated global warming. (4) Soil erosion caused by water and wind is a complicated natural process that has been accelerated by human activity. This erosion has resulted in increasing areas of land degradation which threaten the productive potential of landscapes. Consistent and continuous erosion monitoring will help identify the trends, magnitude, and location of soil erosion. We apply the water-wind erosion model to produce monthly and annual water, and wind erosion estimation at high spatial resolution (up to 90 m, 500 m) for Australia from 2000 to 2020.
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