. (2016) 'A study of non-linearity in rainfall-runo response using 120 UK catchments.', Journal of hydrology., 540 . pp. 423-436. Further information on publisher's website: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. This study presents a catchment characteristic sensitivity analysis concerning the non-linearity 8 of rainfall-runoff response in 120 UK catchments. Two approaches were adopted. The first ap-

Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract Pore-pressure estimation is an important part of oil-well drilling, since drilling into unexpected highly pressured fluids can be costly and dangerous. However, standard estimation methods rarely account for the many sources of uncertainty, or for the multivariate nature of the system. We propose the pore pressure sequential dynamic Bayesian network (PP SDBN) as an appropriate solution to both these issues. The PP SDBN models the relationships between quantities in the pore pressure system, such as pressures, porosity, lithology and wireline log data, using conditional probability distributions based on geophysical relationships to capture our uncertainty about these variables and the relationships between them. When wireline log data is given to the PP SDBN, the probability distributions are updated, providing an estimate of 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 This paper presented here as accepted for publication in Geophysics prior to copyediting and composition. © 2018 Society of Exploration Geophysicists. Page 1 of 47 GEOPHYSICS

Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract.Computer simulators often contain options to include extensions, leading to different versions of a particular simulator with slightly different input spaces. We develop hierarchical emulation, a method for emulating such simulators and for learning about the differences between versions of a simulator. In an example using data from an ocean carbon cycle model, hierarchical emulators outperformed standard emulators both in their predictive accuracy and their coherence with the emulation model. The hierarchical emulator performed particularly well when a comparatively small amount of training data came from the extended simulator. This benefit of hierarchical emulation is advantageous when the extended simulator is costly to run compared to the simpler version.

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Complex systems are often modelled by intricate and intensive computer simulators. This makes their behaviour difficult to study, and so a statistical representation of the simulator is often used, known as an emulator, to enable users to explore the space more thoroughly. These have the disadvantage that they do not allow one to learn about the simulator's behaviour beyond its role as a function from input to output variables. We take a new approach, by involving the internal processes modelled within the simulator in our emulator.We introduce a new technique, intermediate variable emulation, which enables a simulator to be understood in terms of the processes it models. This leads to advantages in simulator improvement and in calibration, as the simulator can be scrutinised in more detail and the physical processes can be used to refine the input space. The intermediate variable emulator also allows one to represent more complicated relationships within the simulator, as we show with a simple example.We demonstrate the method using a simulator of the ocean carbon cycle. Using an intermediate variable emulator we are able to discover unrealistic behaviour in the simulator that would not be noticeable using a standard input to output emulator, and reduce the input space accordingly. We also learn about the sub-processes that drive the output, and about the input variables driving each sub-process. * We are thankful to NERC for funding this research.