Christian OnofImperial College London | Imperial · Department of Civil and Environmental Engineering
Christian Onof
PhD in Engineering; PhD in Philosophy
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164
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Introduction
Skills and Expertise
Publications
Publications (164)
With the help of a physically based recharge‐groundwater flow model and robust detrended fluctuation analysis (r‐DFAn), the effect of local (catchment‐scale) forcing on groundwater levels' scaling behaviour in a riparian aquifer in Wallingford, UK, is investigated. The local forcings investigated in this study include the rainfall's temporal scalin...
The Bartlett-Lewis (BL) model is a stochastic framework for representing rainfall based upon Poisson cluster point process theory. This model has been used for over 30 years in the stochastic modelling of daily and hourly rainfall time series. Historically, the BL model was known to underestimate sub-daily rainfall extremes, but recent advancements...
Rainfall nowcasting (i.e., a forecast over a short time, generally up to 6 h) clearly plays a crucial role in Early Warning Systems (EWSs) concerning water-induced hazards in natural environments.
In fact, an accurate and reliable short-term prediction of weather events is important for public safety from high-impact meteorological events (flash...
This “book” is a copy of the blog discussion "Causality and climate" in Judith Curry’s blog "Climate Etc." taken on 2023-11-11 by Demetris Koutsoyiannis.
Main post by Antonis Christofides, Demetris Koutsoyiannis, Christian Onof and Zbigniew W. Kundzewicz with a comment by Judith Curry.
Featuring 989 contributions in 184 groups from 83 commenters....
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-...
With the help of a physically based recharge-groundwater flow model and robust detrended fluctuation analysis (r-DFAn), the effect of local (catchment-scale) forcing on groundwater levels’ scaling behavior in a riparian aquifer in Wallingford, UK, is investigated. The local forcings investigated in this study include the rainfall’s temporal scaling...
We give a brief overview of conceptions of causality and attempts to find probabilistic characterizations of it. We argue that a useful criterion for causal links in open systems would apply to time-series of causally related phenomena, and that it only makes sense to seek necessary conditions for causality.
The criterion we develop uses an impuls...
This report contains Supplementary Information, namely, mathematical
derivations, justifications and illustrations, for the paper series Revisiting causality using
stochastics (Koutsoyiannis et al., 2022a,b) and in particular its first part, Theory
(Koutsoyiannis et al., 2022a). It comprises three sections, namely Relationship of
continuous- and di...
This report contains Supplementary Information, namely, mathematical
derivations, justifications and illustrations, for the paper series Revisiting causality using
stochastics and in particular its second part, Applications (Koutsoyiannis et al., 2022b). It
comprises three sections, namely Assessment of uncertainty in the identification of the
impu...
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 c...
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 link...
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 (∼4 km, 1 hr) continental‐wide Weather Research Forecasting (WRF) convection‐permitting model un...
A point process model based on a class of Cox processes is developed to analyse precipitation data at a point location. The model is constructed using state-dependent exponential pulses that are governed by an unobserved underlying Markov chain. The mathematical formulation of the model where both the arrival rate of the rain cells and the initial...
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...
Although sub-hourly rainfall temporal characteristics play an important role in occurrences and magnitudes of urban flash floods, most stochastic rainfall models struggle to reproduce them when rainfall information is not available at sub-hourly timescales. This study suggests a simple approach to modifying Poisson cluster rectangular pulse rainfal...
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 ( ∼4km, 1 hr) over the US to assess future changes in hourly precipitation extremes. It is demonstrated that ho...
A stochastic rainfall model that can reproduce various rainfall characteristics at timescales between 5 minutes 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...
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;. Fitting such models essentially relies upon minimising t...
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 o...
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 livin...
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 essential...
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 spati...
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 a...
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 prope...
A novel approach of stochastic rainfall generation that can reproduce various statistical characteristics of observed rainfall at hourly through yearly time scale is presented. The model uses the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model to generate monthly rainfall. Then, it downscales the generated monthly rainfall to the...
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 t...
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,...
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 a warming climate, the moisture holding capacity of the atmosphere is greater which increases the potential for short duration...
We present a web application named Let-It-Rain that is able to generate a 1-hour 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-bas...
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...
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,...
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 regio...
Despite widespread applications of satellite-based precipitation products (SPPs) throughout the TRMM-era, the scarcity of ground-based in-situ data (high density gauge networks, rainfall radar) in many hydro-meteorologically important regions, such as tropical mountain environments, has limited our ability to evaluate both SPPs and individual satel...
Kant and Sartre’s philosophies have both been characterized as centred upon strong notions of freedom. Both thinkers understand freedom as involving the capacity to have done otherwise. These thinkers’ metaphysical accounts of the possibility of freedom are, however, very distinct. While, in Being and Nothingness, Sartre dismisses any form of deter...
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...
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 "singular...
It is a common practice to assign the return period of a given storm event to the urban pluvial flood event that such storm generates. However, this approach may be inappropriate as rainfall events with the same return period can produce different urban pluvial flooding events, i.e., with different associated flood extent, water levels and return p...
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 respons...
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 availabl...
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 t...
In his argument for the possibility of knowledge of spatial objects, in the Transcendental Deduction of the B-version of the Critique of Pure Reason, Kant makes a crucial distinction between space as “form of intuition” and space as “formal intuition.” The traditional interpretation regards the distinction between the two notions as reflecting a di...
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 "singular...
Basic hidden Markov models are very useful in stochastic environmental research but their ability to accommodate sufficient dependence between observations is somewhat limited. However, they can be modified in several ways to form a rich class of flexible models that are useful in many environmental applications. We consider a class of hidden Marko...
This study investigates the impact of rainfall estimates of different spatial resolutions on the hydraulic outputs of the models of four of the EU RainGain project's pilot locations (the Cranbrook catchment (UK), the Herent catchment (Belgium), the Morée‐Sausset catchment (France) and the Kralingen District (The Netherlands)). Two storm events, one...
The Tropical Rainfall Measuring Mission 3B42 precipitation estimates are widely used in tropical regions for hydrometeorological research. Recently, version 7 of the product was released. Major revisions to the algorithm involve the radar reflectivity–rainfall rate relationship, surface clutter detection over high terrain, a new reference database...
Drought risk assessment ideally requires long-term rainfall records especially where inter-annual droughts are of potential concern, and spatially consistent estimates of rainfall to support regional and inter-regional scale assessments. This paper addresses these challenges by developing a spatially consistent stochastic model of monthly rainfall...
An N-dimensional generalized Hurst-Kolmogorov stochastic model is presented that can simulate time-varying spatial geophysical fields, consistent with the observed long-term spatial and temporal persistence. The model is tested through some applications based on time-varying wind velocity field.
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 inten...
The applicability of the operational radar and raingauge networks for urban hydrology is insufficient. Radar rainfall estimates provide a good description of the spatiotemporal variability of rainfall; however, their accuracy is in general insufficient. It is therefore necessary to adjust radar measurements using raingauge data, which provide accur...
The work presented here is a contribution to the Thames Water project of improving the Counters Creek catchment sewerage system in London. An increase in the number of floods affecting basements in the area has indicated the need for improvements to the system. The cost of such improvements could be very high, and as such it is important to determi...
Under future climate scenarios, possible changes of drought patterns pose new challenges for water resources management. For quantifying and qualifying drought characteristics in the UK, the drought severity indices of six catchments are investigated and modelled by two stochastic methods: autoregressive integrated moving average (ARIMA) models and...
Errors in the forcing data are sometimes overlooked in hydrological
studies even when they could be the most important source of
uncertainty. The latter particularly holds true in tropical countries
with short historical records of rainfall monitoring and remote areas
with sparse rain gauge network. In such instances, alternative data such
as the r...
Ideally, hydrological models should be built from equations
parameterised from observed catchment characteristics and data. This
state of affairs may never be reached, but a governing principle in
hydrological modelling should be to keep the number of calibration
parameters to a minimum. A reduced number of parameters to be
calibrated, while mainta...
Global land surface models (LSMs) such as the Joint UK Land Environment Simulator (JULES) are originally developed to provide surface boundary conditions for climate models. They are increasingly used for hydrological simulation, for instance to simulate the impacts of land use changes and other perturbations on the water cycle. This study investig...
Possible changes in drought under future climate scenarios may pose unprecedented challenges for water resources, as well as other environmental and societal issues, and need assessment to quantify their associated risk. Two weather generators, based upon (a) the Neyman-Scott Rectangular Pulses (NSRP) model as implemented by the United Kingdom Clim...
We consider a class of doubly stochastic Poisson process models in the modelling of fine-scale rainfall at multiple gauges in a dense network. Multi-site stochastic point process models are constructed and their likelihood functions are derived. The application of this class of multi-site models, a useful alternative to the widely-known Poisson clu...
This paper considers a class of stochastic point process models, based on doubly stochastic Poisson processes, in the modelling of rainfall. We examine the application of this class of models, a neglected alternative to the widely-known Poisson cluster models, in the analysis of fine time-scale rainfall intensity. These models are mainly used to an...
This paper aims at quantifying the uncertainty on urban runoff associated with the unmeasured small scale rainfall variability, i.e. at a resolution finer than 1 km x 1 km x 5 min which is usually available with C-band radar networks. A case study is done on the 900 ha urban catchment of Cranbrook (London). A frontal and a convective rainfall event...
Global land surface models (LSMs) such as the Joint UK Land Environment Simulator (JULES) are originally developed to provide surface boundary conditions for climate models. They are increasingly used for hydrological simulation, for instance to simulate the impacts of land-use changes and other perturbations on the water cycle. This study 5 invest...
A complete software package for the temporal stochastic simulation of
rainfall process at fine time scales is developed in the R programming
environment. This includes several functions for sequential simulation
or disaggregation. Specifically, it uses the Bartlett-Lewis rectangular
pulses rainfall model for rainfall generation and proven disaggreg...
The assessment of water resources in the Peruvian Andes is particularly
important because the Peruvian economy relies heavily on agriculture.
Much of the agricultural land is situated near to the coast and relies
on large quantities of water for irrigation. The simulation of synthetic
rainfall series is thus important to evaluate the reliability of...
A cluster point process model is considered for the analysis of fine-scale rainfall time series. The model is based on three Poisson processes. The first is a Poisson process of storm origins, where each storm has a random (exponential) lifetime. The second is a Poisson process of cell origins that occur during the storm lifetime, terminating when...