162 results found
Moustakis Y, Fatichi S, Onof CJ, et al., 2022, Insensitivity of ecosystem productivity to predicted changes in fine‐scale rainfall variability, Journal of Geophysical Research: Biogeosciences, Vol: 127, Pages: 1-21, ISSN: 2169-8953
Changes in rainfall associated with climate change are expected to affect the tightly coupled water-carbon ecosystem dynamics. Here, we study the effects of altered rainfall at 33 sites in North America, as projected by the high-resolution/high-fidelity ( ∼ 4km, 1h) continental-wide WRF convection-permitting model under a high-emission scenario (RCP 8.5). We make use of a stochastic weather generator to extend WRF outputs, accounting for natural variability and simultaneously separate the changes in total rainfall, its seasonality, and its intraseasonal pattern. We used these rainfall scenarios to study ecosystem responses with the state-of-the-art Tethys-Chloris terrestrial biosphere model. Model simulations suggest that increases in mean annual rainfall dominate ecosystem responses at dry sites, while wet sites are less sensitive to rainfall changes. Sites of intermediate wetness face reductions in productivity, due to reduced growing season rainfall and increased water losses under altered seasonality, which outpace any possible benefits induced by increases in mean annual totals. Changes in the fine-scale temporal structure of rainfall have an insignificant impact on ecosystem productivity and only alter hydrological dynamics, contradicting expectations based on some field experiments, which, however, are not tailored to directly quantify climate change impacts, but rather to understand the mechanisms leading to ecosystem responses. We further demonstrate how approaches following the ”fewer but larger rainfall events” concept might exacerbate ecosystem responses.
Ramesh N, Rode G, Onof C, 2021, A Cox Process with State-Dependent Exponential Pulses to Model Rainfall, WATER RESOURCES MANAGEMENT, Vol: 36, Pages: 297-313, ISSN: 0920-4741
Onof C, 2021, Kant and the Possibility of Transcendental Freedom, KANT-STUDIEN, Vol: 112, Pages: 343-371, ISSN: 0022-8877
Chen Y, Paschalis A, Wang L-P, et al., 2021, Can we estimate flood frequency with point-process spatial-temporal rainfall models?, Journal of Hydrology, Vol: 600, ISSN: 0022-1694
Stochastic rainfall models are commonly used in practice for long-term flood risk management. One of the most widely used model types is based on point processes. Despite the widespread use of such models, whether their known simplifications in describing the space-time structure of rainfall will affect the accuracy of flood estimation has not been quantified. In this study, we quantify the biases introduced by the rainfall model limitations to flood estimates intwo medium-sized river catchments (717 km2and 844 km2) in the South East of the UK. To achieve this, we used nine years of hourly radar rainfall data, a dense network of hourly rain gauges, a spatial-temporal rainfall stochastic model based on point processes, and a fully distributed hydrological model. We modelled the corresponding catchment water dynamics using observed and simulated hourly rainfall and then assessed whether the errors introduced by the stochastic model will propagate in the river flow dynamics. Our results show that the stochastic rainfall model properly captures the point-scale rainfall statistics, including point extremes and the cross-site spatial correlations. However, the model results in a bias on extremes of areal statistics, including an overestimation of the areal reduction factor, extreme areal mean precipitation, and the areal fraction of rain (wet area ratio). Using this as input for continuous hydrological simulations, we find that the flow duration curves are well preserved, particularly in the high flow seasons (relative bias is less than 7%). The model also reproduces well the flood frequency curves at a daily scale with an averaged relative bias of 0.36-16.9% at 10-year return levels, confirming its ability to infer the long-term flood risk for medium-sized catchments. However, the summer-season hourly peak discharge is highly overestimated with a relative bias of over 163.5% at the same return level. The overestimation in summer hourly peak discharge is3 explained by the
Park J, Cross D, Onof C, et al., 2021, A simple scheme to adjust Poisson cluster rectangular pulse rainfall models for improved performance at sub-hourly timescales, JOURNAL OF HYDROLOGY, Vol: 598, ISSN: 0022-1694
Moustakis Y, Papalexiou SM, Onof CJ, et al., 2021, Seasonality, intensity, and duration of rainfall extremes change in a warmer climate, Earth's Future, Vol: 9, Pages: 1-15, ISSN: 2328-4277
Precipitation extremes are expected to intensify under climate change with consequent impacts in flooding and ecosystem functioning. Here we use station data and high‐resolution simulations from the WRF convection permitting climate model (∼4 km, 1 h) over the US to assess future changes in hourly precipitation extremes. It is demonstrated that hourly precipitation extremes and storm depths are expected to intensify under climate change and what is now a 20‐year rainfall will become a 7‐year rainfall on average for ∼ 75% of gridpoints over the US. This intensification is mostly expressed as an increase in rainfall tail heaviness. Statistically significant changes in the seasonality and duration of rainfall extremes are also exhibited over ∼ 95% of the domain. Our results suggest more non‐linear future precipitation extremes with shorter spell duration that are distributed more uniformly throughout the year.
Chen Y, Paschalis A, Kendon E, et al., 2021, Changing spatial structure of summer heavy rainfall, using convection‐permitting ensemble, Geophysical Research Letters, Vol: 48, ISSN: 0094-8276
Subdaily rainfall extremes have been found to intensify, both from observations and climate model simulations, but much uncertainty remains regarding future changes in the spatial structure of rainfall events. Here, future changes in the characteristics of heavy summer rainfall are analyzed by using two sets (1980–2000, 2060–2080) of 12‐member 20‐year‐long convection‐permitting ensemble simulations (2.2 km, hourly) over the UK. We investigated how the peak intensity, spatial coverage and the speed of rainfall events will change and how those changes jointly affect hourly extremes at different spatial scales. We found that in addition to the intensification of heavy rainfall events, the spatial extent tends to increase in all three subregions, and by up to 49.3% in the North‐West. These changes act to exacerbate intensity increases in extremes for most of spatial scales (North: 30.2%–34.0%, South: 25.8%). The increase in areal extremes is particularly pronounced for catchments with sizes 20–500 km2.
Kim D, Onof C, 2020, A stochastic rainfall model that can reproduce important rainfall properties across the timescales from several minutes to a decade, Journal of Hydrology, Vol: 589, Pages: 1-13, ISSN: 0022-1694
A stochastic rainfall model that can reproduce various rainfall characteristics at timescales between 5 min and one decade is introduced. The model generates the fine-scale rainfall time series using a randomized Bartlett-Lewis rectangular pulse model. Then the rainstorms are shuffled such that the correlation structure between the consecutive storms are preserved. Finally, the time series is rearranged again at the monthly timescale based on the result of the separate coarse-scale monthly rainfall model. The method was tested using the 69 years of 5-minute rainfall data recorded at Bochum, Germany. The mean, variance, covariance, skewness, and rainfall intermittency were well reproduced at the timescales from 5 min to a decade without any systematic bias. The extreme values were also well reproduced at timescales from 5 min to 3 days. The past-7-day rainfall before an extreme rainfall event, which is highly associated with the extreme flow discharge was reproduced well too. The rainstorm shuffling approaches introduced here may be adopted as a standard procedure in combination with any Poisson cluster rainfall model. The methods are simple and parsimonious, yet significantly reduce the systematic underestimation of rainfall variance at coarse scales, and improve the reproduction of skewness, and extreme rainfall depths values at a range of time-scales, thereby addressing well-known shortcomings of Poisson cluster rainfall models.
Moustakis I, Onof CJ, Paschalis A, 2020, Atmospheric convection, dynamics and topography shape the scaling pattern of hourly rainfall extremes with temperature globally, Communications Earth & Environment, Vol: 1, Pages: 1-9, ISSN: 2662-4435
Precipitation extremes (PEx) are expected to increase as ground temperature rises with a rate similarto the air's water holding capacity 7%=K (Clausius-Clapeyron; CC). Recent studies have been inconclusive on the robustness and global consistency of this behavior. Here, we use hourly weatherstations, 40 years of climate reanalysis and two convection permitting models to unravel the globalpattern of PEx scaling with temperature at the hourly scale for the rst time and identify hotspotsof divergence from thermodynamical expectations. We show that in high- and mid-latitudes PExclosely follows a CC scaling, while divergence occurs over the tropics and subtropics. Local features of atmospheric convection, larger-scale dynamics and orography, affect the dependence of PEx on surfacetemperature.
Onof C, Wang L-P, 2020, Modelling rainfall with a Bartlett–Lewis process: new developments, Hydrology and Earth System Sciences, Vol: 24, Pages: 2791-2815, ISSN: 1027-5606
The use of Poisson cluster processes to model rainfall time series at a range of scales now has a history of more than 30 years. Among them, the randomised (also called modified) Bartlett–Lewis model (RBL1) is particularly popular, while a refinement of this model was proposed recently (RBL2; Kaczmarska et al., 2014). Fitting such models essentially relies upon minimising the difference between theoretical statistics of the rainfall signal and their observed estimates. The first statistics are obtained using closed form analytical expressions for statistics of the orders 1 to 3 of the rainfall depths, as well as useful approximations of the wet–dry structure properties. The second are standard estimates of these statistics for each month of the data. This paper discusses two issues that are important for the optimal model fitting of RBL1 and RBL2. The first issue is that, when revisiting the derivation of the analytical expressions for the rainfall depth moments, it appears that the space of possible parameters is wider than has been assumed in past papers. The second issue is that care must be exerted in the way monthly statistics are estimated from the data. The impact of these two issues upon both models, in particular upon the estimation of extreme rainfall depths at hourly and sub-hourly timescales, is examined using 69 years of 5 min and 105 years of 10 min rainfall data from Bochum (Germany) and Uccle (Belgium), respectively.
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.
Onof C, 2020, The role of regulative principles and their relation to reflective judgement, Kant and the Continental Tradition: Sensibility, Nature, and Religion, Pages: 101-130, ISBN: 9781138503748
OchoaRodriguez S, Wang L, Willems P, et 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.
Onof C, 2019, Reality in-itself and the Ground of Causality, KANTIAN REVIEW, Vol: 24, Pages: 197-222, ISSN: 1369-4154
Sione L, Templeton MR, Onof C, et al., 2019, Characterising intermittent water systems in data-scarce settings using a citizen science approach, 17th International Computing and Control for the Water Industry (CCWI) Conference, Exeter, UK
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.
Verbeiren B, Seyoum SD, Lubbad I, et 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.
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.
Cross D, Onof CJ, Winter H, et 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.
Langousis A, Deidda R, Andrei Carsteanu A, et al., 2018, Precipitation measurement and modelling: Uncertainty, variability, observations, ensemble simulation and downscaling, Journal of Hydrology, Vol: 556, Pages: 824-826, ISSN: 0022-1694
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.
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
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