A Bayesian Space-Time Dynamic Linear Model for Radioactivity Deposition after a Nuclear Accident

A three-stage Hierarchical Bayesian Space-Time (HBST) model, an extension of the class of dynamic linear models to space, is proposed for the ground contamination levels (GCL) and related uncertainties caused by polluting discharges in the environment. The model should allow updating the distribution of GCL in time and space as new data in the form of measurements and expert judgements become available to give real-time estimates of deposition levels. An application of the HBST model is proposed for the statistical modelling of radioactivity deposition after a nuclear accident. It explicitly handles uncertainties associated with (i) predictions of depositions from a long-range atmospheric dispersal model, (ii) in-situ gamma ray measurements and (iii) spatial interpolations. Unlike existing environmental statistical models, the HBST model also accounts for an established food chain contamination model called ECOSYS for which it provides data assimilation capabilities. The HBST model permits a fast implementation and full probabilistic inference for the parameters, interpolation and forecasts. Three distinct formulations of the HBST model were applied to assimilate real data of radioactivity deposition from the Chernobyl accident in southern Germany. Two of those formulations differ on the functional form of their spatial covariance matrices while the third, a normal inverse-Wishart model, allows the spatial covariances to "learn" from the data within the usual Bayesian paradigm. The later is shown to outperform the former models both in short and medium term forecasting as well as in a predictive interpolation test that took some measurements as out-of-sample

A Model for Continental-Scale Water Erosion and Sediment Transport and Its Application to the Yellow River Basin

Quantifying suspended sediment discharge at large catchment scales has significant implications for various research fields such as water quality, global carbon and nutrient cycle, agriculture sustainability, and landscape evolution. There is growing evidence that climate warming is accelerating the water cycle, leading to changes in precipitation and runoff and increasing the frequency and intensity of extreme weather events, which could lead to intensive erosion and sediment discharge. However, suspended sediment discharge is still rarely represented in regional climate models because it depends not only on the sediment transport capacity based on streamflow characteristics but also on the sediment availability in the upstream basin. This thesis introduces a continental-scale Atmospheric and Hydrological-Sediment Modelling System (AHMS-SED), which overcomes the limitations of previous large-scale water erosion models. Specifically, AHMS-SED includes a complete representation of key hydrological, erosion and sediment transport processes such as runoff and sediment generation, flow and sediment routing, sediment deposition, gully erosion and river irrigation. In this thesis, we focus on developing and applying AHMS-SED in the Yellow River Basin of China, an arid and semi-arid region known for its wide distribution of loess and the highest soil erosion rate in the world. There are three key issues involving the model development and application: human perturbation (irrigation) of the water cycle, the uncertainty of precipitation forcing on the water discharge and the large-scale water erosion and sediment transport. This thesis addresses all these three issues in the following way. First, a new irrigation module is integrated into the Atmospheric and Hydrological Modelling System (AHMS). The model is calibrated and validated using in-situ and remote sensing observations. By incorporating the irrigation module into the simulation, a more realistic hydrological response was obtained near the outlet of the Yellow River Basin. Second, an evaluation of six precipitation-reanalysis products is performed based on observed precipitation and model-simulated river discharge by the AHMS for the Yellow River Basin. The hydrological model is driven with each of the precipitation-reanalysis products in two ways, one with the rainfall-runoff parameters recalibrated and the other without. Our analysis contributes to better quantifying the reliability of hydrological simulations and the improvement of future precipitation-reanalysis products. Third, a regional-scale water erosion and sediment transport model, referred to as AHMS-SED, is developed and applied to predicting continental-scale fluvial transport in the Yellow River Basin. This model couples the AHMS with the CASCade 2-Dimensional SEDiment (CASC2D-SED) and takes into account gully erosion, a process that strongly affects the sediment supply in the Chinese Loess Plateau. The AHMS-SED is then applied to simulate water erosion and sediment processes in the Yellow River Basin for a period of eight years, from 1979 to 1987. Overall, the results demonstrate the good performance of the AHMS-SED and the upland sediment discharge equation based on rainfall erosivity and gully area index. AHMS-SED is also used to predict the evolution of sediment transport in the Yellow River Basin under specific climate change scenarios. The model results indicate that changes in precipitation will have a significant impact on sediment discharge, while increased irrigation will reduce the sediment discharge from the Yellow River