Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models

In this study we develop a method to estimate the spatially averaged rainfall intensity together with associated level of uncertainty using geostatistical upscaling. Rainfall data collected from a cluster of eight paired rain gauges in a 400 × 200m urban catchment are used in combination with spatial stochastic simulation to obtain optimal predictions of the spatially averaged rainfall intensity at any point in time within the urban catchment. The uncertainty in the prediction of catchment average rainfall intensity is obtained for multiple combinations of intensity ranges and temporal averaging intervals. The two main challenges addressed in this study are scarcity of rainfall measurement locations and non-normality of rainfall data, both of which need to be considered when adopting a geostatistical approach. Scarcity of measurement points is dealt with by pooling sample variograms of repeated rainfall measurements with similar characteristics. Normality of rainfall data is achieved through the use of normal score transformation. Geostatistical models in the form of variograms are derived for transformed rainfall intensity. Next spatial stochastic simulation which is robust to nonlinear data transformation is applied to produce realisations of rainfall fields. These realisations in transformed space are first back-transformed and next spatially aggregated to derive a random sample of the spatially averaged rainfall intensity. Results show that the prediction uncertainty comes mainly from two sources: spatial variability of rainfall and measurement error. At smaller temporal averaging intervals both these effects are high, resulting in a relatively high uncertainty in prediction. With longer temporal averaging intervals the uncertainty becomes lower due to stronger spatial correlation of rainfall data and relatively smaller measurement error. Results also show that the measurement error increases with decreasing rainfall intensity resulting in a higher uncertainty at lower intensities. Results from this study can be used for uncertainty analyses of hydrologic and hydrodynamic modelling of similar-sized urban catchments as it provides information on uncertainty associated with rainfall estimation, which is arguably the most important input in these models. This will help to better interpret model results and avoid false calibration and force-fitting of model parameters

Cosmic rays studied with a hybrid high school detector array

The LORUN/NAHSA system is a pathfinder for hybrid cosmic ray research combined with education and outreach in the field of astro-particle physics. Particle detectors and radio antennae were mainly setup by students and placed on public buildings. After fully digital data acquisition, coincidence detections were selected. Three candidate events confirmed a working prototype, which can be multiplied to extend further particle detector arrays on high schools.Comment: 10 pages, 6 figures. Nigl, A., Timmermans, C., Schellart, P., Kuijpers, J., Falcke, H., Horneffer, A., de Vos, C. M., Koopman, Y., Pepping, H. J., Schoonderbeek, G., Cosmic rays studied with a hybrid high school detector array, Europhysics News (EPN), Vol. 38, No. 5, accepted on 22/08/200

A hydrological model to estimate pollution from combined sewer overflows at the regional scale: Application to Europe

Study region Combined Sewer Overflows (CSO) of 671 Functional Urban Areas (FUAs) throughout the European Union + UK (EU28), representing almost half of the EU28 population. Study focus CSO loads can be quantified at the local scale through measurements, or with calibrated hydrological models. However, they are difficult to quantify at a large scale (e.g. regional or national), due to a lack of data, and the models used at local scale cannot be applied in the absence of knowledge of the combined sewer (CS) network. This paper presents a 6-parameter lumped hydrological model to simulate a CS network and its overflows, using population and rainfall data of 671 EU28 FUAs. New hydrological insights for the region When properly calibrated, the model can predict the CSO hydrographs as well as aggregated CSO descriptors of a catchment with known impervious surface area connected to a CS with a reasonable reliability. When model calibration is not possible, using default values of the parameters enables a first approximation estimate of CSOs, accurate within one order of magnitude, which can be used to support scenario analysis for regional and continental CSO management. At the EU28 scale, the estimated total CSO volume is 5.7·10$^3$ Mm$^3$/y, with a dry weather flow content in CSOs of 460 Mm$^3$/y (assuming a dry weather flow of 200 l/population equivalent (PE)/day including sanitary discharges, industrial discharge and infiltration). A collection of case studies on CSOs is also provided

Polarized radio emission from extensive air showers measured with LOFAR

We present LOFAR measurements of radio emission from extensive air showers. We find that this emission is strongly polarized, with a median degree of polarization of nearly $99\%$, and that the angle between the polarization direction of the electric field and the Lorentz force acting on the particles, depends on the observer location in the shower plane. This can be understood as a superposition of the radially polarized charge-excess emission mechanism, first proposed by Askaryan and the geomagnetic emission mechanism proposed by Kahn and Lerche. We calculate the relative strengths of both contributions, as quantified by the charge-excess fraction, for $163$ individual air showers. We find that the measured charge-excess fraction is higher for air showers arriving from closer to the zenith. Furthermore, the measured charge-excess fraction also increases with increasing observer distance from the air shower symmetry axis. The measured values range from $(3.3\pm 1.0)\%$ for very inclined air showers at $25\, \mathrm{m}$ to $(20.3\pm 1.3)\%$ for almost vertical showers at $225\, \mathrm{m}$. Both dependencies are in qualitative agreement with theoretical predictions.Comment: 22 pages, 14 figures, accepted for publication in JCA

The radio emission pattern of air showers as measured with LOFAR - a tool for the reconstruction of the energy and the shower maximum

The pattern of the radio emission of air showers is finely sampled with the Low-Frequency ARray (LOFAR). A set of 382 measured air showers is used to test a fast, analytic parameterization of the distribution of pulse powers. Using this parameterization we are able to reconstruct the shower axis and give estimators for the energy of the air shower as well as the distance to the shower maximum.Comment: 15 pages, 10 figures, accepted for publication in JCA

A method for high precision reconstruction of air shower Xmax using two-dimensional radio intensity profiles

The mass composition of cosmic rays contains important clues about their origin. Accurate measurements are needed to resolve long-standing issues such as the transition from Galactic to extragalactic origin, and the nature of the cutoff observed at the highest energies. Composition can be studied by measuring the atmospheric depth of the shower maximum Xmax of air showers generated by high-energy cosmic rays hitting the Earth's atmosphere. We present a new method to reconstruct Xmax based on radio measurements. The radio emission mechanism of air showers is a complex process that creates an asymmetric intensity pattern on the ground. The shape of this pattern strongly depends on the longitudinal development of the shower. We reconstruct Xmax by fitting two-dimensional intensity profiles, simulated with CoREAS, to data from the LOFAR radio telescope. In the dense LOFAR core, air showers are detected by hundreds of antennas simultaneously. The simulations fit the data very well, indicating that the radiation mechanism is now well-understood. The typical uncertainty on the reconstruction of Xmax for LOFAR showers is 17 g/cm^2.Comment: 12 pages, 10 figures, submitted to Phys. Rev.