Imperial College London

DrChristianOnof

Faculty of EngineeringDepartment of Civil and Environmental Engineering

Reader in Stochastic Environmental Systems
 
 
 
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Contact

 

+44 (0)20 7594 6006c.onof

 
 
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Location

 

410Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

148 results found

Cross D, Onof C, Winter H, 2020, Ensemble estimation of future rainfall extremes with temperature dependent censored simulation, Advances in Water Resources, Vol: 136, Pages: 1-21, ISSN: 0309-1708

We present a new approach for estimating the frequency of sub-hourly rainfall extremes in a warming climate with simulation by conditioning Bartlett–Lewis rectangular pulse (BLRP) rainfall model parameters on the mean monthly near surface air temperature. We use a censored modelling approach with multivariate regression to capture the sensitivity of the full set of BLRP parameter estimators to temperature enabling the parameter estimators to be updated. The downscaling framework incorporates uncertainty in climate model projections for moderate and severe carbon forcing scenarios by using an ensemble of climate model outputs. Linear regression on the logarithm of BLRP parameter estimators offers a robust model for parameter estimation with uncertainty. The approach is tested with 5 min rainfall data from Bochum in Germany, and Atherstone in the United Kingdom. We find that the approach is highly effective at estimating rainfall extremes in the present climate, and the estimation of future rainfall extremes appears highly plausible.

Journal article

Sione L, Templeton MR, Onof C, Tripathi Set al., 2019, Citizen science to monitor water supply intermittency and quality in developing countries, 17th International Computing and Control for the Water Industry (CCWI) Conference, Exeter, UK

Conference paper

OchoaRodriguez S, Wang L, Willems P, Onof Cet al., 2019, A review of radar‐rain gauge data merging methods and their potential for urban hydrological applications, Water Resources Research, Vol: 55, Pages: 6356-6391, ISSN: 0043-1397

Radar‐rain gauge merging techniques have been widely used to improve the applicability of radar and rain gauge rainfall estimates by combining their advantages, while partially overcoming their individual weaknesses. Despite significant research in this area, guidance on the suitability of and factors affecting merging techniques at the fine spatial‐temporal resolutions required for urban hydrological applications is still insufficient. In this paper, an in‐depth review of radar‐rain gauge merging techniques is conducted, with a focus on their potential for urban hydrological applications. An overview is first given of existing merging techniques and an application‐oriented categorization is proposed: (1) radar bias adjustment methods, (2) rain gauge interpolation methods using radar spatial association as additional information, and (3) radar‐rain gauge integration methods. A detailed review is given of studies focusing on the evaluation and intercomparison of merging methods, based upon which the most widely used and best performing techniques from each category are identified. These are mean field bias adjustment, kriging with external drift, and Bayesian merging. Climatological, operational, and methodological factors affecting merging performance are then reviewed and their relevance for urban applications discussed. Based on this review, conclusions on merging potential for urban applications are drawn and research gaps are identified, which should be addressed to provide further guidance on the use of merging techniques for urban hydrological applications.

Journal article

Onof C, 2019, Reality in-itself and the Ground of Causality, KANTIAN REVIEW, Vol: 24, Pages: 197-222, ISSN: 1369-4154

Journal article

Park J, Onof C, Kim D, 2019, A hybrid stochastic rainfall model that reproduces some important rainfall characteristics at hourly to yearly timescales, Hydrology and Earth System Sciences, Vol: 23, Pages: 989-1014, ISSN: 1027-5606

A novel approach to stochastic rainfall generation that can reproduce various statistical characteristics of observed rainfall at hourly to yearly timescales is presented. The model uses a seasonal autoregressive integrated moving average (SARIMA) model to generate monthly rainfall. Then, it downscales the generated monthly rainfall to the hourly aggregation level using the Modified Bartlett–Lewis Rectangular Pulse (MBLRP) model, a type of Poisson cluster rainfall model. Here, the MBLRP model is carefully calibrated such that it can reproduce the sub-daily statistical properties of observed rainfall. This was achieved by first generating a set of fine-scale rainfall statistics reflecting the complex correlation structure between rainfall mean, variance, auto-covariance, and proportion of dry periods, and then coupling it to the generated monthly rainfall, which were used as the basis of the MBLRP parameterization. The approach was tested on 34 gauges located in the Midwest to the east coast of the continental United States with a variety of rainfall characteristics. The results of the test suggest that our hybrid model accurately reproduces the first- to the third-order statistics as well as the intermittency properties from the hourly to the annual timescales, and the statistical behaviour of monthly maxima and extreme values of the observed rainfall were reproduced well.

Journal article

Verbeiren B, Seyoum SD, Lubbad I, Xin T, ten Veldhuis M-C, Onof C, Wang L-P, Ochoa-Rodriguez S, Veeckman C, Boonen M, See L, Nalpas D, O'Brien B, Johnston A, Willems Pet al., 2018, FloodCitiSense: Early warning service for urban pluvial floods for and by citizens and city authorities, 11th International Conference on Urban Drainage Modelling (UDM), Publisher: Springer, Pages: 660-664

FloodCitiSense aims at developing an urban pluvial flood early warning service for, but also by citizens and city authorities, building upon the state-of-the-art knowledge, methodologies and smart technologies provided by research units and private companies. FloodCitiSense targets the co-creation of this innovative public service in an urban living lab context with all local actors. This service will reduce the vulnerability of urban areas and citizens to pluvial floods, which occur when heavy rainfall exceeds the capacity of the urban drainage system. Due to their fast onset and localized nature, they cause significant damage to the urban environment and are challenging to manage. Monitoring and management of peak events in cities is typically in the hands of local governmental agencies. Citizens most often just play a passive role as people negatively affected by the flooding, despite the fact that they are often the ‘first responders’ and should therefore be actively involved. The FloodCitiSense project aims at integrating crowdsourced hydrological data, collaboratively monitored by local stakeholders, including citizens, making use of low-cost sensors and web-based technologies, into a flood early warning system. This will enable ‘citizens and cities’ to be better prepared for and better respond to urban pluvial floods. Three European pilot cities are targeted: Brussels – Belgium, Rotterdam – The Netherlands and Birmingham – UK.

Conference paper

Ramesh N, Garthwaite A, Onof C, 2018, A doubly stochastic rainfall model with exponentially decaying pulses, Stochastic Environmental Research and Risk Assessment, Vol: 32, Pages: 1645-1664, ISSN: 1436-3240

We develop a doubly stochastic point process model with exponentially decaying pulses to describe the statistical properties of the rainfall intensity process. Mathematical formulation of the point process model is described along with second-order moment characteristics of the rainfall depth and aggregated processes. The derived second-order properties of the accumulated rainfall at different aggregation levels are used in model assessment. A data analysis using 15 years of sub-hourly rainfall data from England is presented. Models with fixed and variable pulse lifetime are explored. The performance of the model is compared with that of a doubly stochastic rectangular pulse model. The proposed model fits most of the empirical rainfall properties well at sub-hourly, hourly and daily aggregation levels.

Journal article

Cross D, Onof CJ, Winter H, Bernardara Pet al., 2018, Censored rainfall modelling for estimation of fine-scale extremes, Hydrology and Earth System Sciences, Vol: 22, Pages: 727-756, ISSN: 1027-5606

Reliable estimation of rainfall extremes is essential for drainage system design, flood mitigation, and risk quantification. However, traditional techniques lack physical realism and extrapolation can be highly uncertain. In this study, we improve the physical basis for short-duration extreme rainfall estimation by simulating the heavy portion of the rainfall record mechanistically using the Bartlett–Lewis rectangular pulse (BLRP) model. Mechanistic rainfall models have had a tendency to underestimate rainfall extremes at fine temporal scales. Despite this, the simple process representation of rectangular pulse models is appealing in the context of extreme rainfall estimation because it emulates the known phenomenology of rainfall generation. A censored approach to Bartlett–Lewis model calibration is proposed and performed for single-site rainfall from two gauges in the UK and Germany. Extreme rainfall estimation is performed for each gauge at the 5, 15, and 60 min resolutions, and considerations for censor selection discussed.

Journal article

Langousis A, Deidda R, Andrei Carsteanu A, Onof C, Burlando P, Uijlenhoet R, Bardossy Aet al., 2018, Precipitation measurement and modelling: Uncertainty, variability, observations, ensemble simulation and downscaling, Journal of Hydrology, Vol: 556, Pages: 824-826, ISSN: 0022-1694

Journal article

Tosunoglu F, ONOF CJ, 2017, Joint modelling of drought characteristics derived from historical and synthetic rainfalls: Application of Generalized Linear Models and Copulas, Journal of Hydrology Regional Studies, Vol: 14, Pages: 167-181, ISSN: 2214-5818

Study regionÇoruh Basin in Northeastern Turkey.Study focusIn recent years, copulas have been widely used to model the joint distribution function of duration and severity series which are the major characteristics of a drought event to be considered in the planning and management of water resources systems. However, as the copula functions are typically fitted to the drought series that are derived from a limited amount of observed data, it may be insufficient to characterize the full range of the analyzed drought characteristics. Therefore, General Linear Models (GLMs) were used to model and simulate rainfall data in this study. The Standard Precipitation Index (SPI) method was used to obtain the drought characteristics from simulated and historical rainfall series. Four Archimedean copulas, namely Ali-Mikhail-Haq, Clayton, Frank and Gumbel-Hougaard, were evaluated to model the joint distribution functions of these characteristics.New hydrological insights for the regionThe Gumbel-Hougaard copula was found to be the most suitable copula in modelling the joint dependence structure of the drought characteristics at five stations in the basin. The derived Gumbel-Hougaard copulas for each station were employed to obtain joint and conditional return periods of the historical and generated drought characteristics. The drought risks that are estimated based on bivariate return periods for different circumstances can provide useful information in planning, management and in assessing adequacy of the water structures in the basin.

Journal article

Schellart ANA, Wang L, Onof C, 2017, High resolution rainfall measurement and analysis in a small urban catchment, 9th International Workshop on Precipitation in Urban Areas: Urban Challenges in Rainfall Analysis, UrbanRain 2012, Publisher: ETH Zurich, Pages: 115-120

Rainfall data from operational radar or rain gauge networks is generally not available at a resolution smaller than 1km 2 . Due to short lead times and high percentage of impervious area, the spatial variability of rainfall becomes important when simulating flow and runoff in smaller urban catchments. In the UK there is a growing interest in modelling rainfall runoff and flooding processes at scales much smaller then 1km 2 . As high density rainfall data are scarce, statistical downscaling techniques are sometimes used to spatially downscale radar or rain gauge data, in order to include the effects of small scale rainfall variability. These downscaling techniques are, however, generally not verified against high resolution rainfall data measured on the ground. This paper describes a study where operational UK radar data has been downscaled to areas between 10 and 100 m, and compared with data from a network of 16 tipping bucket raingauges located in an urban area < 1km 2 .

Conference paper

Lau J, Onof C, 2017, Four radars and three catchments, 9th International Workshop on Precipitation in Urban Areas: Urban Challenges in Rainfall Analysis, UrbanRain 2012, Publisher: ETH Zurich, Pages: 149-153

The East Coast of Peninsular Malaysia is affected by severe rainfall and flooding brought on by moonsoon winds. The government allocation for structural flood control works escalated to USD 1.3 billion between 2005 and 2010. A project was commissioned by the Malaysian Government through the Division of Irrigation and Drainage (DID) in 2009 to investigate the validity of using radar rainfall data records to estimate rainfall rates. The processed radar rainfall data will be used as input into the flood-forecasting model for enhanced estimation of rainfall. The aim is to process radar rainfall data into a suitable format for input into hydrological models. A tool given the name Rainfall Analyzer and Integrator for Malaysia (RAIM) was developed to achieve this. The study required extensive comparisons that were carried out between the radar Quantitative Precipitation Estimates (QPE) and the ground based rain gauges. Seasonal specific empirical Z-R relationships were derived for four radar sites: Kluang, Kota Bharu, Kuantan and Subang radar. A two stage statistical approach was used to obtain the most appropriate Z-R relationship for each of the radar sites. Spatial rainfall estimates were then used as input into HEC-HMS models for three river basins in Johor, Pahang and Kelantan. The response of models to rain-gauge and spatial rainfall was compared. The comparisons concluded that the use of spatial rainfall, as compared to point rain-gauge data produced a better response when compared to recorded river levels.

Conference paper

Vanhaute WJ, Vandenberghe S, Willems P, Onof C, Verhoest NECet al., 2017, Improving extreme value behaviour of fine-scale stochastic point process models, ETH Zurich, 9th International Workshop on Precipitation in Urban Areas: Urban Challenges in Rainfall Analysis, UrbanRain 2012, Pages: 133-137

Urbanization and climate change encourage water managers to improve their ability to predict possible future rainfall events. To study impacts on urban drainage and river systems and assess their vulnerability, long term simulations at fine time scales, including extreme rain storms of high return periods, are of critical importance. Bartlett-Lewis rectangular pulses models are considered to provide such long term simulations. These models have proven to be capable of repro-ducing general historical rainfall characteristics but tend to overestimate extremes at higher levels of aggregation, and underestimate them at lower levels of aggregation. Furthermore, unrealistically large rainfall events are occasionally generated during simulation. This might lead to serious implications when the simulated rainfall series are used for im-pact analysis in urban hydrology. The presented research focuses on ways to improve extreme value behaviour of the Bartlett-Lewis models by introducing the third order moment of rainfall intensity in the objective function. By doing so, the tail of the rainfall distribution is represented better during calibration. The extreme values generated by a standard Bartlett-Lewis model is analysed using the Peak-Over-Threshold method. Secondly, the occasional simulation of unreal-istically large rainfall events is addressed by an adjustment to the model structure. By truncating the gamma distribution responsible for the simulation of cell durations, the probability of sampling extremely long rainfall events is drastically reduced.

Conference paper

Wang L, Onof C, Ochoa-Rodriguez S, Simoes NE, Maksimović Čet al., 2017, On the propagation of rainfall bias and spatial variability through urban pluvial flood modelling, Saint Moritz, Switzerland, 9th International Workshop on Precipitation in Urban Areas: Urban challenges in rainfall analysis, UrbanRain 2012, Publisher: ETH Zurich, Pages: 166-170

The reliability of urban flood modelling can be largely improved if high-accuracy and fine-resolution rainfall estimates are available; however, this requires a very dense network of rainfall sensors and is usually not available due to limited budget and space. Adjustment and downscaling techniques are largely used respectively to post process the radar and rain gauge data to obtain better rainfall estimates in terms of accuracy and resolution. However, the combined application of these two types of techniques was seldom discussed in literatures, and its impact on the subsequent hydraulic modelling is unknown. This work implements a combined procedure of stochastic downscaling and gauge-based adjustment, aiming to evaluate its applicability to urban pluvial flood modelling. Unlike the adjustment process that reduces the rainfall input uncertainty (due to mean bias) through merging rainfall information from different sensors, the stochastic process of generating street-scale rainfall estimates actually causes additional uncertainty (due to spatial variability). This additional uncertainty will further propagate through hydraulic modelling and consequently affect the reliability of the resulting hydraulic outputs. The result of case study suggests that the uncertainty caused by the downscaling process could be larger than that reduced by the adjustment as the drainage area is very small.

Conference paper

Wang L, Onof C, Ochoa S, Simões Net al., 2017, Analysis of kriged rainfields using multifractals, 9th International Workshop on Precipitation in Urban Areas: Urban Challenges in Rainfall Analysis, UrbanRain 2012, Publisher: ETH Zurich, Pages: 138-142

Kriging interpolation is largely used in geostatistics to characterise the spatial structure of data and it is established in general based upon the stationary or intrinsic assumptions; however, the consequence of this second-order approximation is that the local singularities (or extremes) could be smoothed off. This drawback could be magnified as a finer-scale phenomenon is being investigated, such as urban rainfall. Unlike Kriging, the theory multifractals provides a more complete description of the structure of data by considering a range of orders of statistical moments. This work demonstrates the link between multifractal analysis and the Kriging interpolation and finds that Kriging uses only part of in-formation that is included in multifractals. This causes the loss of local singularity of Kriged rainfall field and could be improved by combining it with singularity analysis. A possible solution is proposed in this work and will be implement-ed and presented in the workshop.

Conference paper

McIntyre N, Meng S, Onof CJ, 2016, Incorporating parameter dependencies into temporal downscaling of extreme rainfall using a random cascade approach, Journal of Hydrology, Vol: 542, Pages: 896-912, ISSN: 0022-1694

Downscaling site rainfall from daily to sub-daily resolution is often approached using the multiplicative discrete random cascade (MDRC) class of models, with mixed success. Questions in any application – for MDRCs or indeed other classes of downscaling model - is to what extent and in what way are model parameters functions of rainfall event type and/or large scale climate controls. These questions underlie the applicability of downscaling models for analysing rainfall and hydrological extremes, in particular for synthesising long-term historical or future sub-daily extremes conditional on historic or projected daily data. Using fine resolution data from two gauges in central Brisbane, Australia, covering the period 1908-2015, microcanonical MDRC models are fitted using data from 1 day to 11.25 minute resolutions in seven cascade levels, each level dividing the time interval and its rainfall volume into two sub-intervals. Each cascade level involves estimating: the probabilities that all the rainfall observed in a time interval is concentrated in the first and the second of the two sub-intervals; and also two Beta distribution parameters that define the probability of a given division of the rainfall into both sub-intervals. These parameters are found to vary systematically with time of day, month of year, decade, rainfall volume, event temporal structure and ENSO anomaly. Reasonable downscaling performance is achieved in an evaluation period - in terms of replicating extreme values and autocorrelation structure of 11.25-minute rainfall given the observed daily data - by including the parameter dependence on the rainfall volume and event structure, which involves 16 parameters per cascade level. Using only a volume dependence and assuming symmetrical probability distributions reduces the number of parameters to two per level with only a small loss of performance; and empirical relationships between parameter values and cascade level reduces the total number o

Journal article

Kossieris P, Makropoulos C, Onof C, Koutsoyiannis Det al., 2016, A rainfall disaggregation scheme for sub-hourly time scales: coupling a Bartlett-Lewis based model with adjusting procedures, Journal of Hydrology, Vol: 556, Pages: 980-992, ISSN: 0022-1694

Many hydrological applications, such as flood studies, require the use of long rainfall data at fine time scales varying from daily down to 1 min time step. However, in the real world there is limited availability of data at sub-hourly scales. To cope with this issue, stochastic disaggregation techniques are typically employed to produce possible, statistically consistent, rainfall events that aggregate up to the field data collected at coarser scales. A methodology for the stochastic disaggregation of rainfall at fine time scales was recently introduced, combining the Bartlett-Lewis process to generate rainfall events along with adjusting procedures to modify the lower-level variables (i.e., hourly) so as to be consistent with the higher-level one (i.e., daily). In the present paper, we extend the aforementioned scheme, initially designed and tested for the disaggregation of daily rainfall into hourly depths, for any sub-hourly time scale. In addition, we take advantage of the recent developments in Poisson-cluster processes incorporating in the methodology a Bartlett-Lewis model variant that introduces dependence between cell intensity and duration in order to capture the variability of rainfall at sub-hourly time scales. The disaggregation scheme is implemented in an R package, named HyetosMinute, to support disaggregation from daily down to 1-min time scale. The applicability of the methodology was assessed on a 5-min rainfall records collected in Bochum, Germany, comparing the performance of the above mentioned model variant against the original Bartlett-Lewis process (non-random with 5 parameters). The analysis shows that the disaggregation process reproduces adequately the most important statistical characteristics of rainfall at wide range of time scales, while the introduction of the model with dependent intensity-duration results in a better performance in terms of skewness, rainfall extremes and dry proportions.

Journal article

Leitão JP, Simões NE, Pina RD, Ochoa-Rodriguez S, Onof C, Sá Marques Aet al., 2016, Stochastic evaluation of the impact of sewer inlets’ hydraulic capacity on urban pluvial flooding, Stochastic Environmental Research and Risk Assessment, ISSN: 1436-3240

Sewer inlet structures are vital components of urban drainage systems and their operational conditions can largely affect the overall performance of the system. However, their hydraulic behaviour and the way in which it is affected by clogging is often overlooked in urban drainage models, thus leading to misrepresentation of system performance and, in particular, of flooding occurrence. In the present paper, a novel methodology is proposed to stochastically model stormwater urban drainage systems, taking the impact of sewer inlet operational conditions (e.g. clogging due to debris accumulation) on urban pluvial flooding into account. The proposed methodology comprises three main steps: (i) identification of sewer inlets most prone to clogging based upon a spatial analysis of their proximity to trees and evaluation of sewer inlet locations; (ii) Monte Carlo simulation of the capacity of inlets prone to clogging and subsequent simulation of flooding for each sewer inlet capacity scenario, and (iii) delineation of stochastic flood hazard maps. The proposed methodology was demonstrated using as case study design storms as well as two real storm events observed in the city of Coimbra (Portugal), which reportedly led to flooding in different areas of the catchment. The results show that sewer inlet capacity can indeed have a large impact on the occurrence of urban pluvial flooding and that it is essential to account for variations in sewer inlet capacity in urban drainage models. Overall, the stochastic methodology proposed in this study constitutes a useful tool for dealing with uncertainties in sewer inlet operational conditions and, as compared to more traditional deterministic approaches, it allows a more comprehensive assessment of urban pluvial flood hazard, which in turn enables better-informed flood risk assessment and management decisions.

Journal article

Sunyer MA, Luchner J, Onof C, Madsen H, Arnbjerg-Nielsen Ket al., 2016, Assessing the importance of spatio-temporal RCM resolution when estimating sub-daily extreme precipitation under current and future climate conditions, International Journal of Climatology, Vol: 37, Pages: 688-705, ISSN: 1097-0088

The increase in extreme precipitation is likely to be one of the most significant impacts of climate change in cities due to increased pluvial flood risk. Hence, reliable information on changes in sub-daily extreme precipitation is needed for robust adaptation strategies. This study explores extreme precipitation over Denmark generated by the regional climate model (RCM) HIRHAM-ECEARTH at different spatial resolutions (8, 12, 25 and 50 km), three RCM from the RiskChange project at 8 km resolution and three RCMs from ENSEMBLES at 25 km resolution at temporal aggregations from 1 to 48 h. The performance of the RCM simulations in current climate as well as projected changes for 2081–2100 is evaluated for non-central moments of order 1–3 and for the 2- and 10-year events. The comparison of the RCM simulations and observations shows that the higher spatial resolution simulations (8 and 12 km) are more consistent across all temporal aggregations in the representation of high-order moments and extreme precipitation. The biases in the spatial pattern of extreme precipitation change across temporal and spatial resolution. The hourly extreme value distributions of the HIRHAM-ECEARTH simulations are more skewed than the observational dataset, which leads to an overestimation by the higher spatial resolution simulations. Nevertheless, in general, under current conditions RCM simulations at high spatial resolution represent extreme events and high-order moments better. The changes projected by the RCM simulations depend on the global climate model (GCM)–RCM combination, spatial resolution and temporal aggregation. The simulations disagree on the magnitude and spatial pattern of the changes. However, there is an agreement on higher changes for lower temporal aggregation and higher spatial resolution. Overall, the results from this study show the influence of the spatial resolution on the precipitation outputs from RCMs. The bia

Journal article

Ochoa Rodriguez S, Wang LP, Willems P, Onof Cet al., 2016, A Monte-Carlo Bayesian framework for urban rainfall error modelling, European Geoscience Union General Assembly 2016, Publisher: EGU

Conference paper

Kim D, Cho H, Onof C, Choi Met al., 2016, Let-It-Rain: a web application for stochastic point rainfall generation at ungaged basins and its applicability in runoff and flood modeling, Stochastic Environmental Research and Risk Assessment, Vol: 31, Pages: 1023-1043, ISSN: 1436-3259

We present a web application named Let-It-Rain that is able to generate a 1-h temporal resolution synthetic rainfall time series using the modified Bartlett–Lewis rectangular pulse (MBLRP) model, a type of Poisson stochastic rainfall generator. Let-It-Rain, which can be accessed through the web address http://www.LetItRain.info, adopts a web-based framework combining ArcGIS Server from server side for parameter value dissemination and JavaScript from client side to implement the MBLRP model. This enables any desktop and mobile end users with internet access and web browser to obtain the synthetic rainfall time series at any given location at which the parameter regionalization work has been completed (currently the contiguous United States and Republic of Korea) with only a few mouse clicks. Let-It-Rain shows satisfactory performance in its ability to reproduce observed rainfall mean, variance, auto-correlation, and probability of zero rainfall at hourly through daily accumulation levels. It also shows a reasonably good performance in reproducing watershed runoff depth and peak flow. We expect that Let-It-Rain can stimulate the uncertainty analysis of hydrologic variables across the world.

Journal article

Onof C, 2016, Is there room for nonconceptual content in Kant’s critical philosophy?, Kantian Nonconceptualism, Pages: 199-226, ISBN: 9781137535160

© The Editor(s) (if applicable) and The Author(s) 2016. By examining relevant texts and considering the systematic coherence of Kant’s position, this paper asks whether there is a place for nonconceptual content in his Critical philosophy. Starting with representations with conceptual content, Onof successively examines (i) whether there is more to representations whose conceptual content is well established than is captured by means of concepts, and (ii) the possibility of representations with merely nonconceptual content. With these questions answered in the affirmative, Onof addresses the issue of the dependence of representations with merely nonconceptual content upon those with conceptual content, and thereby distances himself from standard nonconceptualist views. He concludes with some general considerations about the functions of the limited notion of nonconceptual content that the paper identifies.

Book chapter

Onof C, 2016, Drawing on Sartre’s ontology to interpret Kant’s notion of freedom, Comparing Kant and Sartre, Pages: 77-111, ISBN: 9781137454522

Book chapter

Ochoa-Rodriguez S, Wang L, Gires A, Reinoso Rondinel R, Pina RD, van Assel J, Kroll S, Murla Tulys D, Bruni G, Ichiba A, Gaitan S, Cristiano E, Schertzer D, Tchguirinskaia I, Onof C, Willems P, ten Veldhuis JAEet al., 2015, Sensitivity of urban drainage models to the spatial-temporal resolution of rainfall inputs: A multi-storm, multi-catchment investigation, Pontresina, Switzerland, UrbanRain - International Workshop on Precipitation in Urban Areas

Urban hydrological applications require high resolution precipitation and catchment information inorder to well represent the spatial variability, fast runoff processes and short response times ofurban catchments (Berne et al., 2004). Although fast progress has been made over the last fewdecades in high resolution measurement of rainfall at urban scales, including increasing use ofweather radars, recent studies suggest that the resolution of the currently available rainfallestimates (typically 1 x 1 km2in space and 5 min in time) may still be too coarse to meet thestringent requirements of urban hydrology (Gires et al., 2012). What is more, current evidence isstill insufficient to provide a concrete answer regarding the added value of higher resolution rainfallestimates and actual rainfall input resolution requirements for urban hydrological applications. Withthe aim of providing further evidence in this regard, a collaborative study was conducted whichinvestigated the impact of rainfall input resolutions on the outputs of the operational urban drainagemodels of four urban catchments in the UK and Belgium (Figure 1).

Conference paper

Wang L, Ochoa-Rodriguez S, Onof C, Willems Pet al., 2015, Generation of high-temporal resolution QPEs through temporal interpolation of radar images: evaluation over multiple spatial-scales, UrbanRain - International Workshop on Precipitation in Urban Areas

Poster

Nerini D, Zulkafli Z, Wang L-P, Onof C, Buytaert W, Lavado-Casimiro W, Guyot J-Let al., 2015, A Comparative Analysis of TRMM-Rain Gauge Data Merging Techniques at the Daily Time Scale for Distributed Rainfall-Runoff Modeling Applications, Journal of Hydrometeorology, Vol: 16, Pages: 2153-2168, ISSN: 1525-755X

This study compares two nonparametric rainfall data merging methods—the mean bias correction and double-kernel smoothing—with two geostatistical methods—kriging with external drift and Bayesian combination—for optimizing the hydrometeorological performance of a satellite-based precipitation product over a mesoscale tropical Andean watershed in Peru. The analysis is conducted using 11 years of daily time series from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product (also TRMM 3B42) and 173 rain gauges from the national weather station network. The results are assessed using 1) a cross-validation procedure and 2) a catchment water balance analysis and hydrological modeling. It is found that the double-kernel smoothing method delivered the most consistent improvement over the original satellite product in both the cross-validation and hydrological evaluation. The mean bias correction also improved hydrological performance scores, particularly at the subbasin scale where the rain gauge density is higher. Given the spatial heterogeneity of the climate, the size of the modeled catchment, and the sparsity of data, it is concluded that nonparametric merging methods can perform as well as or better than more complex geostatistical methods, whose assumptions may not hold under the studied conditions. Based on these results, a systematic approach to the selection of a satellite–rain gauge data merging technique is proposed that is based on data characteristics. Finally, the underperformance of an ordinary kriging interpolation of the rain gauge data, compared to TMPA and other merged products, supports the use of satellite-based products over gridded rain gauge products that utilize sparse data for hydrological modeling at large scales.

Journal article

Wang LP, Ochoa-Rodriguez S, Onof C, Willems Pet al., 2015, Singularity-sensitive gauge-based radar rainfall adjustment methods for urban hydrological applications, Hydrology and Earth System Sciences, Vol: 19, Pages: 4001-4021, ISSN: 1027-5606

Gauge-based radar rainfall adjustment techniques have been widely used to improve the applicability of radar rainfall estimates to large-scale hydrological modelling. However, their use for urban hydrological applications is limited as they were mostly developed based upon Gaussian approximations and therefore tend to smooth off so-called "singularities" (features of a non-Gaussian field) that can be observed in the fine-scale rainfall structure. Overlooking the singularities could be critical, given that their distribution is highly consistent with that of local extreme magnitudes. This deficiency may cause large errors in the subsequent urban hydrological modelling. To address this limitation and improve the applicability of adjustment techniques at urban scales, a method is proposed herein which incorporates a local singularity analysis into existing adjustment techniques and allows the preservation of the singularity structures throughout the adjustment process. In this paper the proposed singularity analysis is incorporated into the Bayesian merging technique and the performance of the resulting singularity-sensitive method is compared with that of the original Bayesian (non singularity-sensitive) technique and the commonly used mean field bias adjustment. This test is conducted using as case study four storm events observed in the Portobello catchment (53 km2) (Edinburgh, UK) during 2011 and for which radar estimates, dense rain gauge and sewer flow records, as well as a recently calibrated urban drainage model were available. The results suggest that, in general, the proposed singularity-sensitive method can effectively preserve the non-normality in local rainfall structure, while retaining the ability of the original adjustment techniques to generate nearly unbiased estimates. Moreover, the ability of the singularity-sensitive technique to preserve the non-normality in rainfall estimates often leads to better reproduction of the urban drainage syst

Journal article

Ochoa Rodriguez S, Wang L-P, Thraves L, Johnston A, Onof Cet al., 2015, Surface water flood warnings in England: overview, Assessment and recommendations based on survey responses and workshops, Journal of Flood Risk Management, Vol: 11, Pages: S211-S221, ISSN: 1753-318X

Following extensive surface water flooding (SWF) in England in summer 2007, progress has been made in improving the management and prediction of this type of flooding. A rainfall threshold-based extreme rainfall alert (ERA) service was launched in 2009 and superseded in 2011 by the surface water flood risk assessment (SWFRA). Through survey responses from local authorities (LAs) and the outcome of workshops with a range of flood professionals, this paper examines the understanding, benefits, limitations and ways to improve the current SWF warning service. The current SWFRA alerts are perceived as useful by district and county LAs, although their understanding of them is limited. The majority of LAs take action upon receipt of SWFRA alerts, and their reactiveness to alerts appears to have increased over the years and as SWFRA superseded ERA. This is a positive development towards increased resilience to SWF. The main drawback of the current service is its broad spatial resolution. Alternatives for providing localised SWF forecast and warnings were analysed, and a two-tier national-local approach, with pre-simulated scenario-based local SWF forecasting and warning systems, was deemed most appropriate by flood professionals given current monetary, human and technological resources.

Journal article

Simoes NUNO, Ochoa Rodriguez SUSANA, Wang LI-PEN, Pina RUI, Sa Marques A, Onof C, Leitao JOAOet al., 2015, Stochastic Urban Pluvial Flood Hazard Maps Based upon a Spatial-Temporal Rainfall Generator, Water, Vol: 7, Pages: 3396-3406, ISSN: 2073-4441

Journal article

Wang L-P, Ochoa-Rodríguez S, Van Assel J, Pina RD, Pessemier M, Kroll S, Willems P, Onof Cet al., 2015, Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment, Journal of Hydrology, Vol: 531, Pages: 408-426, ISSN: 0022-1694

Rainfall estimates of the highest possible accuracy and resolution are required for urban hydrological applications, given the small size and fast response which characterise urban catchments. While radar rainfall estimates have the advantage of well capturing the spatial structure of rainfall fields and its variation in time, the commonly available radar rainfall products (typically at ∼1 km/5–10 min resolution) may still fail to satisfy the accuracy and resolution – in particular temporal resolution – requirements of urban hydrology. A methodology is proposed in this paper, to produce higher temporal resolution, more accurate radar rainfall estimates, suitable for urban hydrological applications. The proposed methodology entails two main steps: (1) Temporal interpolation of radar images from the originally-available temporal resolutions (i.e. 5–10 min) to finer resolutions at which local rain gauge data are commonly available (i.e. 1–2 min). This is done using a novel interpolation technique, based upon the multi-scale variational optical flow technique, and which can well capture the small-scale rainfall structures relevant at urban scales. (2) Local and dynamic gauge-based adjustment of the higher temporal resolution radar rainfall estimates is performed afterwards, by means of the Bayesian data merging method. The proposed methodology is tested using as case study a total of 8 storm events observed in the Cranbrook (UK) and Herent (BE) urban catchments, for which radar rainfall estimates, local rain gauge and depth/flow records, as well as recently calibrated urban drainage models were available. The results suggest that the proposed methodology can provide significantly improved radar rainfall estimates and thereby generate more accurate runoff simulations at urban scales, over and above the benefits derived from the mere application of Bayesian merging at the original temporal resolution at which radar estimates are available

Journal article

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