Publications
170 results found
Koutsoyiannis D, Onof C, Kundzewicz ZW, et al., 2023, On Hens, Eggs, Temperatures and CO<inf>2</inf>: Causal Links in Earth’s Atmosphere, Sci, Vol: 5
The scientific and wider interest in the relationship between atmospheric temperature (T) and concentration of carbon dioxide ([CO2]) has been enormous. According to the commonly assumed causality link, increased [CO2] causes a rise in T. However, recent developments cast doubts on this assumption by showing that this relationship is of the hen-or-egg type, or even unidirectional but opposite in direction to the commonly assumed one. These developments include an advanced theoretical framework for testing causality based on the stochastic evaluation of a potentially causal link between two processes via the notion of the impulse response function. Using, on the one hand, this framework and further expanding it and, on the other hand, the longest available modern time series of globally averaged T and [CO2], we shed light on the potential causality between these two processes. All evidence resulting from the analyses suggests a unidirectional, potentially causal link with T as the cause and [CO2] as the effect. That link is not represented in climate models, whose outputs are also examined using the same framework, resulting in a link opposite the one found when the real measurements are used.
Onof C, 2023, The unicity, infinity and unity of space, Kantian Review, Vol: 28, Pages: 273-295, ISSN: 1369-4154
The article proposes an interpretation of Kant’s notions of form of, and formal intuition of space to explain and justify the claim that representing space as object requires a synthesis. This involves identifying the transcendental conditions of the analytic unity of consciousness of this formal intuition and distinguishing between it and its content. On this reading which builds upon recent proposals, footnote B160–1n. involves no revision of the Transcendental Aesthetic: space is essentially characterized by non-conceptual features. The article also addresses worries about the infinite magnitude and the unicity of space, by considering the characteristics and requirements of geometric constructions.
Sione L, Templeton MR, Onof C, et al., 2022, Can a citizen science approach to collecting data assist the management of intermittent water supply in low-income and data-scarce settings?, Waterlines, Vol: 41, Pages: 1-19, ISSN: 0262-8104
Intermittent water supplies (IWS) can be both a public health threat and an expensive challenge to address for households, requiring reliance on either costly water storage solutions or alternative water supplies. Despite the fact that IWS are present all over the world, there remains a persistent lack of data on the operation and failures of urban water supply infrastructure in low-income countries. Local government and water utilities tend to be blamed for the poor management of the water supply, and yet there is no established method for reporting or measuring the continuity, reliability, or hours of supply of pipe water delivery. This makes it difficult for water utilities to estimate real economic losses or the investment needed to improve the water supply. Lack of evidence and data on the behaviour of IWS also impedes the development of tailored water management policies, leading to inefficient decision-making from the top down. This paper therefore proposes a method to address the knowledge and data gap on IWS in low-income settings, using citizen science coupled with mobile phone technology to collect data on IWS in a bottom-up approach. The approach was trialled in Kathmandu, Nepal and has since been adopted by the local water supply company.
Koutsoyiannis D, Onof C, Christofidis A, et al., 2022, Revisiting causality using stochastics: 2. applications, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 478, ISSN: 1364-5021
In a companion paper, we develop the theoretical background of a stochastic approach to causality with the objective of formulating necessary conditions that are operationally useful in identifying or falsifying causality claims. Starting from the idea of stochastic causal systems, the approach extends it to the more general concept of hen-or-egg causality, which includes as special cases the classic causal, and the potentially causal and anti-causal systems. The framework developed is applicable to large-scale open systems, which are neither controllable nor repeatable. In this paper, we illustrate and showcase the proposed framework in a number of case studies. Some of them are controlled synthetic examples and are conducted as a proof of applicability of the theoretical concept, to test the methodology with a priori known system properties. Others are real-world studies on interesting scientific problems in geophysics, and in particular hydrology and climatology.
Koutsoyiannis D, Onof C, Christofides A, et al., 2022, Revisiting causality using stochastics: 1. theory, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 478, ISSN: 1364-5021
Causality is a central concept in science, in philosophy and in life. However, reviewing various approaches to it over the entire knowledge tree, from philosophy to science and to scientific and technological applications, we locate several problems, which prevent these approaches from defining sufficient conditions for the existence of causal links. We thus choose to determine necessary conditions that are operationally useful in identifying or falsifying causality claims. Our proposed approach is based on stochastics, in which events are replaced by processes. Starting from the idea of stochastic causal systems, we extend it to the more general concept of hen-or-egg causality, which includes as special cases the classic causal, and the potentially causal and anti-causal systems. Theoretical considerations allow the development of an effective algorithm, applicable to large-scale open systems, which are neither controllable nor repeatable. The derivation and details of the algorithm are described in this paper, while in a companion paper we illustrate and showcase the proposed framework with a number of case studies, some of which are controlled synthetic examples and others real-world ones arising from interesting scientific problems.
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.
Sioné L, Templeton MR, Onof C, 2022, The Challenge of Assisting Stakeholders in Data-Scarce Settings: Characterising Intermittent Water Systems Using A Citizen Science Approach, ISSN: 1755-1307
The lack of relevant data on the failures of water infrastructure impedes efforts to remediate Intermittent Water Systems, and, by association, improve public health. This paper discusses implementing a citizen science approach that leverages the personal concern of citizens to gain information on the quantity and quality of services provided by IWS - data which would otherwise be intractable, laborious or costly, for developing countries to gather. In the first instance, this paper discusses the development of the citizen science approach and the supporting data-collection tool - a smartphone application. It then ascertains: (i) the feasibility and reproducibility of the method, and (ii) the reliability of the collected data by auto-validating the results using internal consistency tests. Finally, the paper reported on the acceptance of the method as a decision-support tool to develop evidence-based solutions to IWS. This paper demonstrates the first proof of concept that citizen science can be used in conjunction with personal communication technology to bridge the information gaps on the supply of municipal water in data-scarce and resource-constrained settings. It is a feasible and validated data collection method which requires a minimal time and resource investment.
Ramesh N, Rode G, Onof C, 2022, 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
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Onof C, Chen Y, Wang L-P, et al., 2021, A two-stage analogue model for real-time urban flood forecasting
<jats:p>&lt;p&gt;In this work a two-stage (rainfall nowcasting + flood prediction) analogue model for real-time urban flood forecasting is presented. The proposed approach accounts for the complexities of urban rainfall nowcasting while avoiding the expensive computational requirements of real-time urban flood forecasting.&lt;/p&gt;&lt;p&gt;The model has two consecutive stages:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;&lt;strong&gt;(1) Rainfall nowcasting: &lt;/strong&gt;0-6h lead time ensemble rainfall nowcasting is achieved by means of an analogue method, based on the assumption that similar climate condition will define similar patterns of temporal evolution of the rainfall. The framework uses the NORA analogue-based forecasting tool (Panziera et al., 2011), consisting of two layers. In the &lt;strong&gt;first layer, &lt;/strong&gt;the 120 historical atmospheric (forcing) conditions most similar to the current atmospheric conditions are extracted, with the historical database consisting of ERA5 reanalysis data from the ECMWF and the current conditions derived from the US Global Forecasting System (GFS). In the &lt;strong&gt;second layer&lt;/strong&gt;, twelve historical radar images most similar to the current one are extracted from amongst the historical radar images linked to the aforementioned 120 forcing analogues. Lastly, for each of the twelve analogues, the rainfall fields (at resolution of 1km/5min) observed after the present time are taken as one ensemble member. Note that principal component analysis (PCA) and uncorrelated multilinear PCA methods were tested for image feature extraction prior to applying the nearest neighbour technique for analogue selection.&lt;/li&gt;&lt;li&gt;&lt;strong&gt;(2) Flood prediction: &
Chen Y, Paschalis A, Peleg N, et al., 2021, Reparametrizing rainfall generators with convective-permitting models to generate high-resolution rainfall for climate impact studies
<jats:p>&lt;p&gt;A high-resolution rainfall data at a km and sub-hourly scales provides a powerful tool for hydrological risk assessment in the current and the future climate. Global circulation models or regional circulation models generally provide projections at much coarser space-time resolutions of 10-100 kilometres and daily to monthly. In the recent decade, convection-permitting models (CPM) have been developed and enable the projection at a kilometre and sub-hourly scales. CPMs, due to their very high computational demand, are still limited to a small number of ensemble simulations. This limits their skill in hydrology, where quantification of extremes and their variability is essential for risk assessment and design. In this project, we propose the combined use of CPMs with stochastic rainfall generators to simulate ensemble of climate change at hydrologically relevant scales.&lt;/p&gt;&lt;p&gt;To achieve this, we used the STREAP space-time stochastic rainfall generator, a 1 km resolution composite rain radar data and a 2.2km CPM dataset from the UK Met Office. For the mid-land region of the UK, we parameterised STREAP for the present climate using rainfall observations. CPM simulations were used to derive the change of STREAP parameters with a changing climate. These parameters describe the change in weather patterns, the rainfall intensification, and changes in the structure of rainfall. Our results show that by combining a physics-based model and a stochastic weather generator we can simulate robust ensemble of rainfall at a minimal computational cost while preserving all physical attributes from climate change projections.&lt;/p&gt;</jats:p>
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.
Onof C, 2021, Kant's Conception of Freedom. A Developmental and Critical Analysis, STUDI KANTIANI, Vol: 34, Pages: 231-236, ISSN: 1123-4938
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, Editors: Baiasu, Vanzo, Publisher: ROUTLEDGE, Pages: 101-130, ISBN: 978-1-138-50374-8
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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.
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