Design Earthquakes in the U.K.

Three sites in the UK are taken, representative of low, medium and high hazard levels (by UK standards). For each site, the hazard value at 10⁻⁴ annual probability is computed using a generic seismic source model, and a variety of ground motion parameters: peak ground acceleration (PGA), spectral acceleration at 10 Hz and 1 Hz, and intensity. Disaggregation is used to determine the nature of the earthquakes most likely to generate these hazard values. It is found (as might be expected) that the populations are quite different according to which ground motion parameter is used. When PGA is used, the result is a rather flat magnitude distribution with a tendency to low magnitude events (\le 4.5 ML) which are probably not really hazardous. Hazard-consistent scenario earthquakes computed using intensity are found to be in the range 5.8–5.9 ML, which is more in accord with the type of earthquake that one expects to be a worst-case event in the UK. This revised version was published online in July 2006 with corrections to the Cover Date.     

A new magnitude category disaggregation approach for temporal high‐resolution rainfall intensities

Simulation of quick runoff components such as surface runoff and associated soil erosion requires temporal high‐resolution rainfall intensities. However, these data are often not available because such measurements are costly and time consuming. Current rainfall disaggregation methods have shortcomings, especially in generating the distribution of storm events. The objectives of this study were to improve point rainfall disaggregation using a new magnitude category rainfall disaggregation approach. The procedure is introduced using a coupled disaggregation approach (Hyetos and cascade) for multisite rainfall disaggregation. The new procedure was tested with ten long‐term precipitation data sets of central Germany using summer and winter precipitation to determine seasonal variability. Results showed that dividing the rainfall amount into four daily rainfall magnitude categories (1–10, 11–25, 26–50, >50 mm) improves the simulation of high rainfall intensity (convective rainfall). The Hyetos model category approach (Hyetos_Cat) with seasonal variation performs representative to observed hourly rainfall compared with without categories on each month. The mean absolute percentage accuracy of standard deviation for hourly rainfall is 89.7% in winter and 95.6% in summer. The proposed magnitude category method applied with the coupled Hyetos_Cat–cascade approach reproduces successfully the statistical behaviour of local 10‐min rainfall intensities in terms of intermittency as well as variability. The root mean square error performance statistics for disaggregated 10‐min rainfall depth ranges from 0.20 to 2.38 mm for summer and from 0.12 to 2.82 mm for the winter season in all categories. The coupled stochastic approach preserves the statistical self‐similarity and intermittency at each magnitude category with a relatively low computational burden. Copyright © 2014 John Wiley & Sons, Ltd.     

岩石颗粒解离试验研究

试验确定，在用HCI和NaOH试剂解离硅质胶结的沉积岩时，浓度5％的试剂即可达到解离度95％以上，且颗粒表面特征、重矿物数量和重矿物标型特征较浓度10％的试剂处理的样品保存得更好。     

Evaluation of an empirical orthogonal function–based method to downscale soil moisture patterns based on topographical attributes

Soil moisture influences many hydrologic applications including agriculture, land management and flood prediction. Most remote‐sensing methods that estimate soil moisture produce coarse resolution patterns, so methods are required to downscale such patterns to the resolutions required by these applications (e.g. 10‐ to 30‐m grid cells). At such resolutions, topography is known to affect soil moisture patterns. Although methods have been proposed to downscale soil moisture based on topography, they usually require the availability of past high‐resolution soil moisture patterns from the application region. The objective of this article is to determine whether a single topographic‐based downscaling method can be used at multiple locations without relying on detailed local observations. The evaluated downscaling method is developed on the basis of empirical orthogonal function (EOF) analysis of space–time soil moisture data at a reference catchment. The most important EOFs are then estimated from topographic attributes, and the associated expansion coefficients are estimated on the basis of the spatial‐average soil moisture. To test the portability of this EOF‐based method, it is developed separately using four data sets (Tarrawarra, Tarrawarra 2, Cache la Poudre and Satellite Station), and the relationships that are derived from these data sets to estimate the EOFs and expansion coefficients are compared. In addition, each of these downscaling methods is applied not only for the catchment where it was developed but also to the other three catchments. The results suggest that the EOF downscaling method performs well for the location where it is developed, but its performance degrades when applied to other catchments. Copyright © 2011 John Wiley & Sons, Ltd.     

Bayesian area-to-point kriging using expert knowledge as informative priors

Area-to-point (ATP) kriging is a common geostatistical framework to address the problem of spatial disaggregation or downscaling from block support observations (BSO) to point support (PoS) predictions for continuous variables. This approach requires that the PoS variogram is known. Without PoS observations, the parameters of the PoS variogram cannot be deterministically estimated from BSO, and as a result, the PoS variogram parameters are uncertain. In this research, we used Bayesian ATP conditional simulation to estimate the PoS variogram parameters from expert knowledge and BSO, and quantify uncertainty of the PoS variogram parameters and disaggregation outcomes. We first clarified that the nugget parameter of the PoS variogram cannot be estimated from only BSO. Next, we used statistical expert elicitation techniques to elicit the PoS variogram parameters from expert knowledge. These were used as informative priors in a Bayesian inference of the PoS variogram from BSO and implemented using a Markov chain Monte Carlo algorithm. ATP conditional simulation was done to obtain stochastic simulations at point support. MODIS (Moderate Resolution Imaging Spectroradiometer) atmospheric temperature profile data were used in an illustrative example. The outcomes from the Bayesian ATP inference for the Matérn variogram model parameters confirmed that the posterior distribution of the nugget parameter was effectively the same as its prior distribution; for the other parameters, the uncertainty was substantially decreased when BSO were introduced to the Bayesian ATP estimator. This confirmed that expert knowledge brought new information to infer the nugget effect at PoS while BSO only brought new information to infer the other parameters. Bayesian ATP conditional simulations provided a satisfactory way to quantify parameters and model uncertainty propagation through spatial disaggregation.     

A steepness index unit volume flood hydrograph approach for sub‐daily flow disaggregation

The estimation of sub‐daily flows from daily flood flows is important for many hydrological and hydraulic applications. Flows during flood events often vary significantly within sub‐daily time‐scales, and failure to capture the sub‐daily flood characteristic can result in an underestimation of the instantaneous flood peaks, with possible risk of design failure. It is more common to find a longer record of daily flow series (observed or modelled using daily rainfall series) than sub‐daily flow data. This paper describes a novel approach, known as the steepness index unit volume flood hydrograph approach, for disaggregating daily flood flows into sub‐daily flows that takes advantage of the strong relationship between the standardized instantaneous flood peak and the standardized daily flood hydrograph rising‐limb steepness index. The strength of this relationship, which is considerably stronger than the relationship between the standardized flood peak and the event flood volume, is shown using data from six rivers flowing into the Gippsland Lakes in southeast Australia. The results indicate that the steepness index unit volume flood hydrograph approach can be used to disaggregate modelled daily flood flows satisfactorily, but its reliability is dependent on a model's ability to simulate the standardized daily flood hydrograph rising‐limb steepness index and the event flood volume. Copyright © 2007 John Wiley & Sons, Ltd.     

Propagation of uncertainties in interpolated rainfields to runoff errors

Conditional daily rainfields were generated using collocated raingauge radar data by a kriging interpolation method, and disaggregated into hourly rainfields using variants of the method of fragments. A geographic information system (GIS)-based distributed rainfall–runoff model was used to convert the hourly rainfields into hydrographs. Using the complete radar rainfall as input, the rainfall–runoff model was calibrated based on storm events taken from nested catchments. Performance statistics were estimated by comparing the observed and the complete radar rainfall simulated hydrographs. Degradation in the hydrograph performance statistics by the simulated hourly rainfields was used to identify runoff error propagation. Uncertainty in daily rainfall amounts alone caused higher errors in runoff (depth, peak, and time to peak) than those caused by uncertainties in the hourly proportions alone. However, the degradation, which reduced with runoff depth, caused by the combined uncertainties was not significantly different from that caused by the uncertainty of amounts alone.     

Seismic hazard disaggregation in performance‐based earthquake engineering: occurrence or exceedance?

Seismic hazard disaggregation is commonly used as an aid in ground‐motion selection for the seismic response analysis of structures. This short communication investigates two different approaches to disaggregation related to the exceedance and occurrence of a particular intensity. The impact the different approaches might have on a subsequent structural analysis at a given intensity is explored through the calculation of conditional spectra. It is found that the exceedance approach results in conditional spectra that will be conservative when used as targets for ground‐motion selection. It is however argued that the use of the occurrence disaggregation is more consistent with the objectives of seismic response analyses in the context of performance‐based earthquake engineering. Copyright © 2015 John Wiley & Sons, Ltd.     

A cluster-optimizing regression-based approach for precipitation spatial downscaling in mountainous terrain

Precipitation temporal and spatial variability often controls terrestrial hydrological processes and states. Common remote-sensing and modeling precipitation products have a spatial resolution that is often too coarse to reveal hydrologically important spatial variability. A statistical algorithm was developed for downscaling low-resolution spatial precipitation fields. This algorithm auto-searches precipitation spatial structures (rain-pixel clusters), and orographic effects on precipitation distribution without prior knowledge of atmospheric setting. It is composed of three components: rain-pixel clustering, multivariate regression, and random cascade. The only required input data for the downscaling algorithm are coarse-pixel precipitation map and a topographic map. The algorithm was demonstrated with 4 km × 4 km Next Generation Radar (NEXRAD) precipitation fields, and tested by downscaling NEXRAD-aggregated 16 km × 16 km precipitation fields to 4 km × 4 km pixel precipitation, which was then compared to the original NEXRAD data. The demonstration and testing were performed at both daily and hourly temporal resolutions for the northern New Mexico mountainous terrain and the central Texas Hill Country. The algorithm downscaled daily precipitation fields are in good agreement with the original 4 km × 4 km NEXRAD precipitation, as measured by precipitation spatial structures and the statistics between the downscaling and the original NEXRAD precipitation maps. For three daily precipitation events, downscaled precipitation map reproduces precipitation variance of the disaggregation field, and with Pearson correlation coefficients between the downscaled map and the NEXRAD map of 0.65, 0.71, and 0.80. The algorithm does not perform as well on downscaling hourly precipitation fields at the examined scale range (from 16 km to 4 km), which underestimates precipitation variance of the disaggregation field. For a scale range from 4 km to 1 km, the algorithm has potential to perform well at both daily and hourly precipitation fields, indicated from good regression performance.