Job Type: Principal or Senior Flood Risk Engineer

Job Summary

We are seeking a motivated Flood Risk Principal or Senior Engineer/Scientist to join our Civil and Environmental Engineering Consultancy. This role sits within our Flood Risk and Environment Team, a group of highly skilled and experienced engineers and scientists working at the forefront of fields in our sector. The opportunity offers a balance of field work and office based assessments, contributing to the development of sustainable solutions for a range of exciting projects and clients.

Role Responsibilities

• Perform a wide range of flood risk assessment work including (but not limited to) Flood Risk Assessments, Sustainable Drainage strategies, river diversions, flood defence design, assessment of hydrology and hydraulic river modelling in a variety of industry-standard software
• Given the small and agile nature of Water Environment, there are frequently opportunities across our wider team, and therefore broader interests are desirable. This will vary depending on workload and expertise, but could also include civil engineering design of in-channel improvements and structures, and environmental assessment work.
• Handle and delegate associated reporting, GIS, and drawings. We pride ourselves on ensuring that published outputs are delivered to an exceptionally high standard.
• Collaborate with scientists and engineers in other disciplines, both internally and in partnership with external consultants, as part of a holistic approach to find the best solution for our clients.
• Project, program, resource, client and financial management is required from the outset, and, taking responsibility for more complex projects.
• Mentoring more junior members of staff forms a vital part of the role. This element would include review of technical reports, providing constructive feedback and supporting decision making.
• Business development including fee quoting, bids and tenders.

Qualifications, Required Skills and Experience

Minimum requirements

• A relevant undergraduate engineering or science degree with 5-10 years of experience in the flood risk field.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork abilities. This includes client management, and the ability to engage with stakeholders and regulators.
• Knowledge of range of engineering software (e.g., Microsoft Office Suite, AutoCAD, QGIS).
• Knowledge of a range of hydraulic modelling software (HEC-RAS, ISIS-TUFLOW, Flood Modeller, Infoworks)
• Ability to work independently, with minimal supervision, and managing more junior employees.

Desirable

• A post-graduate degree is desirable, but not essential.
• A full, clean UK driving licence is desirable to facilitate site work in rural areas.
• It would be ideal to be a member of CIWEM or other relevant institution, with ambitions to pursue chartership in future.
• Whilst our work is typically South-East based, our projects do take us all over the UK and therefore willingness for occasional longer distance travel.

We are actively recruiting a diverse workforce, with wide range of skillsets and areas of expertise. One of the benefits of our small size is the ability to adapt, offering new services as and when the market demands, and we are always keen to hear from engineers with experience in other areas. We understand that not everyone will fit a specific job profile, please still apply – you may have other skills we have not considered yet.

About Us

Water Environment is a leading consultancy in the built and natural environment, providing innovative solutions to public and private-sector clients. We are committed to sustainability and excellence in all our projects.

Benefits

• Primarily office-based in Surbiton, London with occasional remote working around site visits and remote meetings
• Whilst the ideal work pattern would be full-time to support our growing team, we recognise that balancing work with other responsibilities can be challenging and are happy to talk flexible working and part-time options for the right candidate.
• Competitive salary
• 5% pension contribution
• Generous leave allowances
• Group life and critical illness insurance
• Opportunities for professional development and training (with allocated training budget and paid professional memberships each year)
• Exposure to a wide range of projects and clients from the outset, with opportunities for growth and expansion of your own interests. We frequently provide specialist technical support for larger consultancies and are involved in some major government projects and frameworks, despite our small size.
• Supportive and collaborative work environment, opportunities to learn from, and teach, our bright and enthusiastic team at all levels (from graduate to director).
• Relaxed office environment with excellent camaraderie and social events.

How to Apply

If this sounds like the perfect next step for you, we’d love to hear from you.

Apply now with your CV or reach out for an informal chat: careers@waterenvironment.co.uk

Abstract:

Due to the spread of urbanisation and increased environmental awareness, odour has become a major problem in communities surrounding landfills. The aim of this research was to investigate odour emissions from landfills and develop a management tool that operators could use to assist in minimising the impacts of odour. The management tool would be in the form of real-time perditions of odour concentrations in the vicinity of a source. The Bisasar Road landfill in Springfield, Durban was a case study site for the research.

The methodologies used in this project can be divided into three broad categories. Firstly, flow visualisation experiments were conducted on the case study site to investigate the effects of complex terrain and the results compared to predictions from a dispersion model. Secondly, source characterisation was done on-site. Sources of odour were identified using a portable odour monitor (Electronic nose). Sources of odour were then sampled using sorbent tubes and analysis done using Gas Chromatography – Mass Spectrometry. Thirdly, numerical dispersion modelling was done. Five available dispersion models were assessed and compared against one another in order to select the most suitable model for this application. A software management tool or ‘Odour Management System’ (OMS,) was designed and implemented on a computer at the Bisasar Road landfill.

Qualitative results of the flow visualisation experiments show that terrain does have an effect on a dispersing plume path for short-range predictions. Comparisons between the flow experiments and model predictions are qualitatively consistent. Qualitative results were not obtained for the emission flow rate and emission concentration of landfill gas. The chemical composition of the fresh waste gas was determined. ADMSTM (Advances Dispersion Modelling System) was found to be the most suitable dispersion model for this application. The OMS has been installed on-site to produce odour concentration graphics every ten minutes. A fence line odour control system has been installed along approximately 600 metres of the landfill border based on work done as part of this project. Weather conditions and information provided by the OMS, assist in running the odour control system economically.

Abstract:

The emission of offensive odour from landfill sites is a nuisance that generates public opposition to the existence of these facilities. The aim of our research is to develop technology for an effective management system for landfill operations that minimizes the negative impacts of odour on the surrounding communities. The key requirements of the system are reviewed. We highlight the effects of complex terrain on odour dispersion and the role of concentration fluctuations in odour predictions. Results from a case study site are presented to illustrate the application and potential benefits of an odour management system.

Odour emissions from landfill sites generate public opposition to the existence of landfills. Our research aims to develop technology for an effective management and decision support system for landfill operations that will minimise the negative impacts of odour on surrounding communities. In this paper we review the key requirements of such a system. In particular we highlight the effects of complex terrain on the flow field and dispersion, and the need for accurate modelling of concentration fluctuations in order to predict odour effects.

The Bisasar Road landfill site in Durban, South Africa, is being used as a case study in the research. A review of odour complaints is reported and the associated weather conditions discussed. Field experiments using smoke flares for flow visualization are described and the results compared qualitatively with numerical flow modelling. Simulations using a prototype odour management system are reported to illustrate the effectiveness of using such a system to mitigate odour impacts.

An “Odour Management System” (OMS) has been developed to generate graphical predictions of dilution of gas from emission sources on a “real-time” basis. Particular focus has been on odour from the open working face at landfills. An OMS has been implemented at Bisasar Road landfill site in Durban, South Africa. The predictions of dilution are displayed in graphical form on a PC (Personal Computer) on-site at Bisasar Road. Durban Solid Waste (DSW) initiated the research behind the development of the OMS in an attempt to minimise the impact of odour from the landfill on the communities neighbouring Bisasar Road landfill site.

This paper briefly introduces Bisasar road landfill site and reviews the problems associated with odour and the impact on residents living near the landfill site. Attempts to determine the emission rate and concentration of odour from the uncovered working face (“fresh waste gas”) are discussed. The sampling done and results obtained are reviewed. This paper also describes the OMS and its components. Full details of the research carried out during the development of the OMS as well as detailed information on the OMS and its applications can be found in Laister (2002). This paper describes how the community is being used to validate predictions.

What is this new version of the String of Beads Model?

Weather radar measures rainfall rates over a large area in fine spatial detail and very frequently. This translates into a circular area up to 200km across with 1km resolution every 5 minutes. In this study, the diameter of the circle is reduced to 128km for precision and manageability, even so, each image representing the estimated rain rate over the area is described by up to 12 868 numbers. A year’s data can quickly fill a computer disk unless it is managed cleverly. What to do with all the information that these data represent? The answer is to summarize it using a model.

The String of Beads model was introduced in WRC contract K5/752 when the “Bead” (the model of the rainfall process while it is raining) was developed. In this study the model has been refined and extended to improve the “Bead” process and model the arrival of the beads of space-time rain on the “String” of time.

The new version of the model has the particularly useful property that it can be calibrated on historical daily raingauge data, even in the absence of a radar in the area, although this would be an added advantage. The daily raingauges (if there is a reasonably good network covering more than 40 years) characterize the inter-annual and intra-annual variability of the rainfall. This means that the model can be used in places where radar data is short or even absent.

The model is designed to perform two important modelling tasks. The first is simulation of long sequences (of many years duration) to conduct “what if” studies of rainfall over the target area. The output from such simulations matches the refined gridded data of digital elevation models now being used in catchment rainfall/runoff studies. The second task to which the model can be adapted is to give short-term forecasts (nowcasts in the literature) of future rainfall for use in real-time flood forecasting in either rural or urban environments. Using the String of Beads model to forecast rainfall on a catchment up to one or two hours ahead has the benefit that managers (catchment, flood or disaster) have extra information to enable them to make anticipatory decisions – they can be proactive rather than reactive when a flood threatens.

Why is the String of Beads model good for rainfall modelling? What makes it a better model than others?

Space-time rainfall is a very complicated physical process and might seem to need a very complicated model to describe it. The advantage of the model described here is that it is relatively straightforward in that it is defined by a very few parameters and achieves remarkable realism in the synthetic sequences it generates. The result is that it is remarkably fast in execution, which is a necessary characteristic with all the detail it presents.

It was the first model of its sort to present spatial detail and realistic temporal development in one package. There are others which are based on point rainfall models which can capture the average behaviour over an area with time and those which can model rainfall events with similar detail, but none that did both before the String of Beads model was devised. It is also adaptable to a variety of rainfall regimes – it has been fitted to South African, Swiss, German and English data with success and has become well known internationally as a serious competitor in the rainfield modelling environment.

Why is the String of Beads model good for rainfall modelling? What makes it a better model than others?

Space-time rainfall is a very complicated physical process and might seem to need a very complicated model to describe it. The advantage of the model described here is that it is relatively straightforward in that it is defined by a very few parameters and achieves remarkable realism in the synthetic sequences it generates. The result is that it is remarkably fast in execution, which is a necessary characteristic with all the detail it presents.

It was the first model of its sort to present spatial detail and realistic temporal development in one package. There are others which are based on point rainfall models which can capture the average behaviour over an area with time and those which can model rainfall events with similar detail, but none that did both before the String of Beads model was devised. It is also adaptable to a variety of rainfall regimes – it has been fitted to South African, Swiss, German and English data with success and has become well known internationally as a serious competitor in the rainfield modelling environment.

What sorts of Simulations are envisaged?

The crucial characteristics that a space-time model of rainfall must capture are

• the storm arrivals,
• their duration and intensity while it is raining and also
• move realistically in the right directions and at the right velocities.

Each of these characteristics must be quantified and their mutual dependence and variability accounted for. The way the new model works is to examine the “String” of time and identify the alternating wet and dry periods on it. These lengths vary randomly (and we have discovered, independently of each other) but can be characterized by the lognormal probability distribution function whose two parameters vary periodically over the year. This is the basic process.

Once it is raining, the “String” supports a “Bead” of space-time rainfall. The “Beads” have the lengths of the wet periods on the “String” and their structure in space and time defines the behaviour of the rainfall event. These descriptors are its velocity and direction and whether it describes, on the one hand, patchy thunder-storm rainfall or, on the other hand, widespread rain of less variability but longer duration on average. The new model achieves all this and passes the stringent statistical and visual tests to check whether the result is good.

Can the “String of Beads” model be used for Forecasting?

Yes. The “String of Beads” model in its new form is particularly suitable for forecasting. This is because of its efficient structure based on time series ideas. In the previous formulation, the set of images comprising the “Bead” were all generated simultaneously using Fourier transforms in a box of space-time. Although mathematically elegant, this was not practical because it was difficult to generate wet periods of the correct length and the method did not lend itself to forecasting applications.

The new model, by contrast, is based on time series ideas, in that images are constructed in series using linear combinations of the images in the recent past with added noise, both at image scale and at pixel scale. This is convenient for simulation because the wet period can be defined to be any length to suit the “String” process and the computation method is more efficient and quicker than previously. This is because the dependence structure is relatively short – only the previous five images are needed to construct the new one.

Forecasting is a natural extension of this formulation of the model. At any time that the data are available (which is possible with a radar dedicated to the catchment concerned) the previous five images are used to construct the possible future an image at a time until the forecast horizon has been achieved. It is found that forecasts are good to start with, deteriorating gradually so that they are still relatively useful an hour ahead, but useless after two hours. For urban applications in particular the methodology has particular promise for “buying time” to help managers to anticipate disaster.

A stochastic space-time model of rainfall is proposed, which is defined by a small number of parameters and models rainfall intensity images measured by radar. It is a phenomenological (rather than a physically based) model which provides sequences of realistic stochastically generated rainfields over an area covered by an S-band weather radar sited near Bethlehem, South Africa. The weather types studied vary from predominantly scattered convective thunder-shower type rainfall through to widespread, general, stratiform rainfields with high Wetted Area Ratios. The methods of analysis and modelling of rainfall events as measured by radar are described. A three dimensional (two space and one time) simulated rainfall event designed to mimic a real sequence is generated, analysed and compared to the observed rainfall event. Not only does it reproduce the statistics without obvious bias, it has been validated using the Generalized structure function. Variograms (specialisations of the Generalized structure function) fitted to the data measured by radar from a variety of rainfield types suggest that the correlation distance in rainfields is limited to between 12 and 25 km. The implication is that although the rainfields appear to be locally non-stationary, they are stationary over areas typically measured by radar. This is a post-justification for using methods based on the assumption that rainfields are stationary random fields and allows the use of straightforward statistical and spectral techniques which are the heart of the String of Beads model. This paper concentrates on modelling the rainfall process during a wet period, deferring the treatment of the inter-arrival of wet and dry periods to a follow-on paper.

The String of Beads model is a space-time model of rainfields measured by weather radar. It is here driven by two auto-regressive time series models, one at the image scale, the other at the pixel scale, to model the temporal correlation structure of the wet-period process. The marginal distribution of the pixel scale intensities on a given radar-rainfall image is described by a lognormal distribution. The spatial dependence structure of each image is defined by a power spectrum approximated by a power law function with a negative exponent. It is demonstrated that this stochastic modelling approach is valid because the images sampled are effectively stationary above a scale of 30 km, which is less than a quarter of the image width. By advecting a simulated sequence of images along the same cumulative advection vector as the observed event and matching the image-scale statistics of each simulated image with those of the corresponding observed image, a simulated sequence of plausible images is generated which mimics (has the same space-time statistics as) the observed event but differs from it in detail. Aggregating the pixel scale intensities in each sequence over a number of time and space intervals and then comparing their spatial and temporal statistics, demonstrates that the model captures the intermediate scale behaviour well, showing satisfactorily its ability to downscale rainfall in space and time. The model thus has potential as an operational space-time model of rainfields.

What is the background to this research?

A valuable measure of the country’s potentially available water resources is through rainfall measurements. These measurements help with the quantitative definition of drought and possible climatic change, besides providing information for the design of structures such as spillways, bridges, culverts, channels, storm-water drains etc. On-line measurements of rainfall can help reduce the potential damage of floods; the calculations of runoff from flood-producing storms can be projected into the future to forecast flows in sensitive areas in flood-plains near rivers and through water-bodies stored behind dams.

Traditionally, rainfall measurement has been made using 120mm and 200mm diameter rain-gauges scattered randomly over the country. In South Africa the largest proportion of these were (and still are) read by volunteer observers at 8:00am each day and the daily totals collected by the South African Weather Bureau (SAWB). There was a time (1950) when 4500 or more such gauges were “live” on any particular day, but the number of daily read raingauges has dropped to 1750 in 1999 with less than 600 of these reporting daily for the public good; the numbers are still diminishing.

There is thus a need for remote sensing of rainfall using radar and satellite imagery. These technically advanced, relatively recent innovations provide detailed information about rain-rates over large areas in sequences of images which are typically at 5 minute intervals from radar and 30 minute intervals from satellite platforms. They thus have the potential to provide detailed information (in space and time) of rain rates which can be used in many applications.

Why is the String of Beads model good for rainfall modelling? What makes it a better model than others?

Space-time rainfall is a very complicated physical process and might seem to need a very complicated model to describe it. The advantage of the model described here is that it is relatively straightforward in that it is defined by a very few parameters and achieves remarkable realism in the synthetic sequences it generates. The result is that it is remarkably fast in execution, which is a necessary characteristic with all the detail it presents.

It was the first model of its sort to present spatial detail and realistic temporal development in one package. There are others which are based on point rainfall models which can capture the average behaviour over an area with time and those which can model rainfall events with similar detail, but none that did both before the String of Beads model was devised. It is also adaptable to a variety of rainfall regimes – it has been fitted to South African, Swiss, German and English data with success and has become well known internationally as a serious competitor in the rainfield modelling environment.

What did the research programme originally set out to achieve?

The contractual objectives were:

1. To provide a means of describing rainfields in both time and space by means of fractals.
2. To facilitate the modelling of rainfields with economical mathematical descriptors.
3. To use the project as a means of enhancing collaboration between institutions in the KwaZulu Natal region.

The first of these objectives was modified from using fractals to Gaussian Random Fields, with better effect, and has been achieved successfully with the alternative approach. The second objective has been achieved if one interprets the words “with economical” as “parsimoniously with a small number of”, as was originally intended. The third objective was achieved in a different sense than was originally intended, because although little collaboration initially developed in KwaZulu Natal proper, other than through the participation of the University of Zululand in the steering committee, the collaboration between researchers in Civil Engineering at Natal University and in the Bethlehem Precipitation Research Programme has become strong.

Why model rainfall measured by radar in space-time?

Increasing demands are being made on our meagre water resources and because there is greater population pressure causing invasion of high-risk flood-prone areas, there is an increased need for more detailed information about the spatial and temporal distribution of rainfall to assist with flood warning and mitigation.

In general the purpose of modelling is two-fold: to summarise complex physical processes in a descriptive or mathematical way with as few parameters as possible (parsimony) and to gain as deep an understanding of the phenomena as is possible, relevant and cost effective under the circumstances (insight).

The reason for modelling the rainfall process is to provide as much parsimony and insight as is reasonable, so that possible future scenarios can be artificially generated to conduct “what-if” investigations. Because of the relatively recent advent of remote sensing of rainfall in South Africa, the radar-records are quite short (3 to 5 years) and do not provide, in themselves, an objective basis for some aspects of decision-making. However there is a relatively large archive of data collected from daily read rain gauges which are too sparse, in many instances to provide the spatial detail required in some analyses/studies but give the information needed to quantify the variability of the rainfall processes over a long time-base. It is therefore prudent and desirable to marry the long-term (but poor spatial) information in the archive of daily rain-gauge records with the finely detailed (but short) records obtained from radar.

What happened to the Fractals?

Fractal models have been proposed as appropriate candidates for modelling rainfields because, by their construction, they preserve certain statistical properties, amongst them self-similarity, scaling etc. The mathematics is difficult and the models used (in particular the multiplicative models) have had the tendency to produce “blocky” rainfields which are distinguishable from typical radar measured rainfields and not easily manipulated to give a movement of the rainfield. By contrast, the approach adopted here was to use the KISS principle, employing standard statistical analysis methods (yet exploiting what had been discovered about scaling fields in the search for appropriate fractal models) to craft a parsimonious, robust, adaptable, feasible model which is statistically correct and visually realistic when compared to typical radar images of areal rainfall. The outcome is a model which is relatively easy to understand, fit and apply, without becoming entangled in the thicket of arcane terminology which tends to make the subject of fractals unapproachable to all but the intrepid.

Why the “String-of Beads” model?

Rainfall events (storms, showers, light rain etc) are patchy in space and patchy in time but are (in a wide sense) “wet”. The time between rainfall events (when it is not raining on a given area) is evidently “dry”. Modelling the space-time process of rainfall over an area for a few days, months or years thus consists of defining the process of alternating wet and dry periods and modelling the time-series structure. This is a one-dimensional process in time. Once the rain starts falling on the area during a wet period, the spatial as well as the temporal behaviour of the rainfall phenomenon is of paramount interest:

• how does the rain cover the area?
• how quickly do storm cells grow and decay?
• what is the speed and direction of the storm?

are some of the questions that demand answers.

How is the model fitted to the data?

The one-dimensional “string” describing the wet/dry process is defined by the sequence of wet and dry days on an area as specified by the rain gauge records. This process can be thought of as the alternation of three types of “climate” (or weather) occurring on the days of the observed record. The types are dry, scattered rain (usually caused by convective storms) and general rain (usually caused by stratiform, large systems). The distinction is a matter of convenience because there are days when the weather is a mixture of convective and stratiform clouds producing rain. This process is modelled by a three-state Markov chain which was thoroughly validated as a good model in WRC contract No. 550 conducted by Pegram and Seed (Report No. 550/1/98).

The three-dimensional space-time “bead” of a given rain event is defined by a sequence of images called Constant Altitude Plan Position Indicators (CAPPIs) which are derived from weather radar measurements. These images are sampled at approximately 5-minute intervals. Where more than 3% of the area covered by the CAPPI records rainfall above 1mm/hr, the image is classified as wet, otherwise the image is part of the “dry” time. Each CAPPI image is analysed to extract three parameters characterising its 9000 or more data points. This parsimonious use of parameterisation was one of the objectives of the original contract. These statistics (s, ß and µ) are stored for use in the simulation/generation process.

How is a sequence of storms generated?

In generation, the process of analysis is reversed. First a plausible sequence of climate-type is generated. When (for example) a scattered rainday is encountered, a length of storm is randomly generated (usually between 2 and 4 hours in the late afternoon) and a reasonable set of values for the parameters s, ß and µ chosen. The rainfall event is then produced by power-law filtering (in the complex domain) some Fourier transformed white noise, which is then reverse transformed, scaled and exponentiated to produce a sequence of CAPPI images. These are manipulated to provide realistic velocities of the storm-cells across the design area.

Does the model work well?

The answer is that it does with surprising facility and veracity. The sequence of images of CAPPIs generated for a wet period, not only have the right statistics and clustering behaviour, they are visually indistinguishable from the real images they mimic. This may seem like a quaint comment, but the production of images which look like the “real thing” is very important for the end user.

How could the model be used in practice?

In order for the model to realise its potential usefulness in application, a link must be formed between the parameterisation of the model and the statistics of raingauges recording one day totals in a region of interest. The proportion of raingauges recording rain on a particular day within a circle of diameter 130km (which should contain about 50 raingauges) is a good indicator of the Wetted Area Ratio, which is likely to have a close link with the parameters of the String of Beads model. The follow-on project (K5/1010) will investigate these links and allow the model to be widely applied.