Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

PublishedThis is the final version of the article. It first appeared from Copernicus Publications via http://dx.doi.org/10.5194/hess-20-903-2016The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash–Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A better precipitation product does not guarantee a better discharge simulation because of interactions. It is also found that a good discharge simulation depends on a good coalition of a hydrological model and a precipitation product, suggesting that, although the satellite-based precipitation products are not as accurate as the gauge-based products, they could have better performance in discharge simulations when appropriately combined with hydrological models. This information is revealed for the first time and very beneficial for precipitation product applications.This study was supported by the National Natural Science Foundation of China (grant no. 1320105010 and 51279021). The first author gratefully acknowledges the financial support provided by the China Scholarship Council

Patterns of Scalable Bayesian Inference

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with few clear overarching principles. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. We review existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, we characterize the general principles that have proven successful for designing scalable inference procedures and comment on the path forward

Bayesian Uncertainty Analysis and Decision Support for Complex Models of Physical Systems with Application to Production Optimisation of Subsurface Energy Resources

Important decision making problems are increasingly addressed using computer models for complex real world systems. However, there are major limitations to their direct use including: their complex structure; large numbers of inputs and outputs; the presence of many sources of uncertainty; which is further compounded by their long evaluation times. Bayesian methodology for the analysis of computer models has been extensively developed to perform inference for the physical systems. In this thesis, the Bayesian uncertainty analysis methodology is extended to provide robust decision support under uncertainty. Bayesian emulators are employed as a fast and efficient statistical approximation for computer models. We establish a hierarchical Bayesian emulation framework that exploits known constrained simulator behaviour in constituents of the decision support utility function. In addition, novel Bayesian emulation methodology is developed for computer models with structured partial discontinuities. We advance the crucial uncertainty quantification methodology to perform a robust decision analysis developing a technique to assess and remove linear transformations of the utility function induced by sources of uncertainty to which conclusions are invariant, as well as incorporating structural model discrepancy and decision implementation error. These are encompassed within a novel iterative decision support procedure which acknowledges utility function uncertainty resulting from the separation of the analysts and final decision makers to deliver a robust class of decisions, along with any additional information, for further consideration. The complete toolkit is successfully demonstrated via an application to the problem of optimal petroleum field development, including an international and commercially important benchmark challenge