Publications
170 results found
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 .
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.
Vanhaute WJ, Vandenberghe S, Willems P, et 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.
Wang L, Onof C, Ochoa-Rodriguez S, et 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.
Wang L, Onof C, Ochoa S, et 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.
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
Kossieris P, Makropoulos C, Onof C, et 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.
Leitão JP, Simões NE, Pina RD, et 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.
Sunyer MA, Luchner J, Onof C, et 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
Ochoa Rodriguez S, Wang LP, Willems P, et al., 2016, A Monte-Carlo Bayesian framework for urban rainfall error modelling, European Geoscience Union General Assembly 2016, Publisher: EGU
Kim D, Cho H, Onof C, et 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.
Onof C, 2016, Is there room for nonconceptual content in Kant’s critical philosophy?, Kantian Nonconceptualism, Pages: 199-226, ISBN: 9781137535160
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.
Onof C, 2016, Drawing on Sartre’s ontology to interpret Kant’s notion of freedom, Comparing Kant and Sartre, Pages: 77-111, ISBN: 9781137454522
Ochoa-Rodriguez S, Wang L, Gires A, et 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).
Wang L, Ochoa-Rodriguez S, Onof C, et 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
Nerini D, Zulkafli Z, Wang L-P, et 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.
Wang LP, Ochoa-Rodriguez S, Onof C, et 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
Ochoa Rodriguez S, Wang L-P, Thraves L, et 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.
Simoes NUNO, Ochoa Rodriguez SUSANA, Wang LI-PEN, et al., 2015, Stochastic Urban Pluvial Flood Hazard Maps Based upon a Spatial-Temporal Rainfall Generator, Water, Vol: 7, Pages: 3396-3406, ISSN: 2073-4441
Wang L-P, Ochoa-Rodríguez S, Van Assel J, et 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
Ochoa-Rodriguez S, Wang L-P, Gires A, et al., 2015, Impact of Spatial and Temporal Resolution of Rainfall Inputs on Urban Hydrodynamic Modelling Outputs: A Multi-Catchment Investigation, Journal of Hydrology, Vol: 531, Pages: 389-407, ISSN: 0022-1694
Urban catchments are typically characterised by high spatial variability and fast runoff processes resulting in short response times. Hydrological analysis of such catchments requires high resolution precipitation and catchment information to properly represent catchment response. This study investigated the impact of rainfall input resolution on the outputs of detailed hydrodynamic models of seven urban catchments in North-West Europe. The aim was to identify critical rainfall resolutions for urban catchments to properly characterise catchment response. Nine storm events measured by a dual-polarimetric X-band weather radar, located in the Cabauw Experimental Site for Atmospheric Research (CESAR) of the Netherlands, were selected for analysis. Based on the original radar estimates, at 100 m and 1 min resolutions, 15 different combinations of coarser spatial and temporal resolutions, up to 3000 m and 10 min, were generated. These estimates were then applied to the operational semi-distributed hydrodynamic models of the urban catchments, all of which have similar size (between 3 and 8 km2), but different morphological, hydrological and hydraulic characteristics. When doing so, methodologies for standardising model outputs and making results comparable were implemented. Results were analysed in the light of storm and catchment characteristics. Three main features were observed in the results: (1) the impact of rainfall input resolution decreases rapidly as catchment drainage area increases; (2) in general, variations in temporal resolution of rainfall inputs affect hydrodynamic modelling results more strongly than variations in spatial resolution; (3) there is a strong interaction between the spatial and temporal resolution of rainfall input estimates. Based upon these results, methods to quantify the impact of rainfall input resolution as a function of catchment size and spatial-temporal characteristics of storms are proposed and discussed.
Onof C, Schulting D, 2015, Space as Form of Intuition and as Formal Intuition: On the Note to B160 in Kant's <i>Critique of Pure Reason</i> (vol 124, pg 1, 2015), PHILOSOPHICAL REVIEW, Vol: 124, Pages: 297-297, ISSN: 0031-8108
Wang LP, Ochoa-Rodríguez S, Onof C, et al., 2015, Singularity-sensitive gauge-based radar rainfall adjustment methods for urban hydrological applications, Hydrology and Earth System Sciences Discussions, Vol: 12, Pages: 1855-1900, ISSN: 1812-2116
Luchner J, Sunyer MA, Madsen H, et al., 2015, Sub-daily extreme precipitation under current and future climate conditions from high resolution RCMs
The increase in extreme precipitation is likely to be one of the most significant impacts of climate change in cities, where short duration extreme precipitation is one of the main causes for severe floods. Hence, reliable information on changes in sub-daily extreme precipitation is needed for the design of robust adaptation strategies.
Onof C, 2015, X Physics: Kant’s lectures on physics and the development of the critical philosophy, Reading kant's Lectures, Pages: 461-483, ISBN: 9783110342321
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Onof C, Schulting D, 2015, Space as Form of Intuition and as Formal Intuition: On the Note to B160 in Kant's <i>Critique of Pure Reason</i>, PHILOSOPHICAL REVIEW, Vol: 124, Pages: 1-58, ISSN: 0031-8108
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Ochoa-Rodriguez S, Wang L, Bailey A, et al., 2015, Evaluation of radar-rain gauge merging methods for urban hydrological applications: relative performance and impact of gauge density, Pontresina, Switzerland, UrbanRain - International Workshop on Precipitation in Urban Areas
Kaczmarska J, Isham V, Onof C, 2014, Point process models for fine-resolution rainfall, Hydrological Sciences Journal/Journal des Sciences, Hydrologiques, Vol: 59, Pages: 1972-1991, ISSN: 0262-6667
In a recent development in the literature, a new temporal rainfall model, based on the Bartlett-Lewis clustering mechanism and intended for sub-hourly application, was introduced. That model replaced the rectangular rain cells of the original model with finite Poisson processes of instantaneous pulses, allowing greater variability in rainfall intensity over short intervals. In the present paper, the basic instantaneous pulse model is first extended to allow for randomly varying storm types. A systematic comparison of a number of key model variants, fitted to 5-min rainfall data from Germany, then generates further new insights into the models, leading to the development of an additional model extension, which introduces dependence between rainfall intensity and duration in a simple way. The new model retains the original rectangular cells, previously assumed inappropriate for fine-scale data, obviating the need for the computationally more intensive instantaneous pulse model.
Ramesh NI, Onof C, 2014, A class of hidden Markov models for regional average rainfall, HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, Vol: 59, Pages: 1704-1717, ISSN: 0262-6667
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Skaugen T, Onof C, 2014, A rainfall-runoff model parameterized from GIS and runoff data, HYDROLOGICAL PROCESSES, Vol: 28, Pages: 4529-4542, ISSN: 0885-6087
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- Citations: 28
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