History of Hydrology Wiki – Interview with Keith Beven

Have you seen the wiki “History of Hydrology“? It was created by Professor Keith Beven, Professor Emeritus in the Lancaster Environment Centre, in 2016, and we have recently interviewed him to learn more about it:

What was your motivation behind creating the “History of Hydrology Wiki”?

I have always been interested in the history of the hydrology and hydraulics, which is both long and interesting.

A lot of the early history was summarized in the book on the History of Hydrology, written by Asit K Biswas (North-Holland, 1971) but only up to the end of the 19th Century. There have been a few history studies since, including my own papers on Robert Horton in Journal of Hydrology and Hydrological Processes in 2004, but not a lot.

Both AGU and the International Association of Hydrogeologists have collected some video interviews with eminent hydrologists and hydrogeologists, but clearly there are now very many who we cannot now interview about their work and collaborations.

I remember realizing that I had once met Walter Langbein at a meeting, when he must have been close to retirement. He had worked with Robert Horton but, as a very young hydrologist at that time, I had not realized and certainly had not thought to ask about it. It would be a shame if the history just faded away.

So it is a project I have had in mind for some time, but it is not something that hydrologists are going to get much academic credit for. More of a project for someone who has retired, so I started the site when I took my pension (albeit that I have not exactly retired from research yet).

We have seen that a page was recently posted to Max Adam Kohler, a pioneer in hydrological forecasting. This area of hydrology has long been seen as an “operational” activity. Do you think hydrological forecasting can also be considered as “science” within hydrological sciences?

That is an interesting question (with a long history). Max Kohler, who died only last month at the age of 102, was influential in setting up hydrological forecasting in the US, but also working for the international community as Head of the Hydrological Division at WMO.

As in meteorology, we would want hydrological forecasts to be based on the best available science – but unlike meteorology, hydrological responses are very much dominated by the boundary conditions including catchment characteristics rather than the process representations. In particular in extreme flood events, we cannot be sure that we have a good idea of the volume of inputs from the raingauge and radar data available to the forecaster (and as yet numerical weather prediction cannot provide better estimates). And while we can be pretty sure of observations of water levels, there is a lot of uncertainty associated with estimates of the corresponding discharges. Thus, even the water balance equation may not be that useful in forecasting.

There are certainly cases where there is apparently more discharge than rainfall (and much less than expected when, in mountainous areas, valley bottom raingauges do not reflect that the precipitation falls as snow at higher elevations). Thus in these cases, until we have better data or NWP predictions, data assimilation can be more important as a way of getting improved forecasts  than improved process representations.

Thus in the Lancaster Flood Forecasting Methodology we use a nonlinear transform of the observed inputs, with a linear transfer function, coupled with a simple data assimilation algorithm to produce forecasts and their associated uncertainties. That does not mean that either the nonlinearity or transfer function should not be considered as scientific – see the hypothetico-inductive arguments in Peter Young’s  article in Water Resources Research, 2013.

If you search for ‘ensemble’ on the wiki then one only gets one hit to a reference, whilst there are 8 results when you check for ‘uncertainty’ – are there trends  or fashions one can observe through history?

I think you have to remember that all the biographies (with one exception who will be 100 years old next month) are of hydrologists who have already died, and that running ensembles of models has only been possible since the later part of the 20th Century (my own first Monte Carlo experiments were carried out in 1980 on a CDC “Mainframe” computer. There will certainly be more mention of both model ensembles and uncertainties in the biographies of those who have been active since then. I am sure that trends and fashions will be evident in the material as it accumulates. That, after all, is what constitutes the history of a subject area, especially when (as in hydrology) dramatic innovations occur only rarely.

Finally, how do you think the HEPEX community can contribute to the “History of Hydrology Wiki”?

As a Wiki site, anyone can register to add or edit material on the site (at the moment I am acting as the moderator for entries), or can send me material. There are templates on the site for the different types of entry which can be biographies, histories of experimental catchments, histories of hydrological institutions and details of historical hydrological textbooks (it is somewhat surprising how texts from the early 20th Century look similar to those of the early 21st Century).

I have also recently added a section on annotated papers about the history of hydrology. The hope is that the materials will provide both a record of our history and a starting point for anyone wanting to do more detailed studies. We particularly need entries from the non-English speaking world which is certainly under-represented as yet. There is no reason why entries should not be in another language (see for example the entry for Eugène Belgrand), but it would be good if an English summary can be provided, with a link to the original (see Pierre Cappus).

There will be a session at EGU in Vienna in 2018 convened Okke Batelaan, Roberto Ranzi, Laurent Pfister and myself. With this session we aim to stimulate the discussion on how we, as a community, develop a historical literacy and integrate this in teaching and research to enhance our science. We warmly invite your contributions that discuss how hydrological concepts have gradually evolved over time; how forgotten methods might have contemporary value; the value of historical datasets of experimental catchments and their management; remarkable contributions of scientists, institutes and organisations.   Contributions from the HEPEX Community would be welcome – including (why not) something on the origins and history of the HEPEX project. A more detailed description of the session and the link for abstract submission can be found here. The abstract submission deadline is 10 January 2018, 13:00 CET.

Thank you for this interview and thoughts.

We encourage all Hepex members to contribute to the wiki and EGU session. Sessions on Hydrological Forecasting (science and applications!) will also be held at EGU 2018 (see them in a previous post) and abstract submissions to them are also welcome.

Posted in activities, historical, interviews, opinion | Leave a comment

Hydropower management in Brazil and water forecasts – Interview with Alberto Assis dos Reis

Alberto is an engineer and hydrologist at Cemig, a Brazilian power company headquartered in Belo Horizonte, the capital of the state of Minas Gerais, and is currently starting a PhD work at the Federal University of Minas Gerais (UFMG). His PhD project involves also collaboration with three other organizations in Europe, strongly involved in Hepex: Irstea (France), Deltares (The Netherlands) and ECMWF (UK). He was recently visiting these organizations and, when he came to France, I took the opportunity to ask him some questions:

Maria-Helena Ramos: In a few words, how do you describe the Brazilian electric system?

Alberto Assis dos Reis: Currently, the system has an installed capacity of about 152 GW, the largest in South America, and it is essentially hydrothermal, with a great part of hydraulic generation (nearly 65% of domestic supply). In fact, Brazil has an electric matrix that is predominantly of renewable sources (74.6% of the electricity of domestic supply), including an important share of biomass (from sugar cane) and wind power. By 2024, it is expected that hydropower and biomass relative part will slightly decrease, although they will remain important sources of power generation. Wind power and solar power are expected to increase their part in the Brazilian electricity generation in the near future.

Today, the system is regulated by the “Operator of the National Electricity System” (ONS), which is a non-profit private entity created in 1998. The ONS is responsible for the coordination and control of the generation and transmission installations in the National Interconnected System (SIN). The SIN comprises the electricity companies in the South, South-East, Center-West, North-East and part of the North region, including Cemig, the company I have been working for since 2002.

MHR: How important are streamflow forecasts to the management of hydropower production in Brazil?

AAR: Brazil has a large capacity for water storage; I think it is one of the greatest, if not the greatest, in the world. The inflows to hydroelectric plants have a considerable weight in the planning of the operation of the electrical system, as well as a large weight in the energy price setting in the short term market, the SDP – Settlement of Differences Price.

The calculation of the SDP is based on what we call MCO – Marginal Cost of Operation, which is limited by a minimum and a maximum price, both set annually by the NEEA – National Electric Energy Agency (in Portuguese, Agência Nacional de Energia Elétrica, ANEEL). The MCO is obtained from computer models run by the ONS. These models optimize the system’s operation, solving the hydrothermal dispatch problem in Brazil.

These models run once a week, every Thursday, with flow forecasts. Forecasts are issued on a weekly basis for the first five weeks and on a monthly basis for the next months. Currently, hydrologic models are used for the first week, ARMA models are used for the following weeks, and synthetic flow series are generated for up to 5 years, based on historic flow records from 1931 to today.

Streamflow flow forecasts and reservoir levels are variables with a strong role as price definers. It is estimated that they account for about 60% of the price definition. It is thus important to have hydrologic forecasts of good quality when taking decisions on selling and buying energy in the market.

MHR: Why have you decided to do a PhD work on ensemble forecasting?

AAR: It is a long story! At Cemig, we have been working on the development of robust seasonal forecasting system for several years. Our aim is to have an efficient operation of the hydropower plants with state-of-the-art products. We have been using medium-range ensemble weather forecasts (up to 15 days) and the ESP (Ensemble Streamflow Prediction) technique with a hydrological model operated in house, calibrated for a large set of catchments in Brazil.

As you know, the operationalization of hydrologic ensemble modeling is a fairly laborious activity to perform manually, because it means repeating several times the data collection activities, the preparation of files for models and their application in hydrological models. We have thus put efforts into the development of tools to automate this process. Since 2014, in collaboration with Deltares and LACTEC, we have the FEWS-Cemig system running, now in operational mode, over 40 operational centers of water forecast.

I have been involved in this project since the beginning and now that everything is running more or less automatically I have more time to spend on experting the forecasts. This means that I have more ideas on how to improve them! I am curious to know if we can improve our systems and make even better forecasts. I am also interested in running a seamless forecasting system, from short- to long-range forecasts in a coherent, and unique, framework.

That’s why I decided to do a PhD work: to explore further techniques to improve the quality of the forecasts in a seamless system and evaluate its impact on the management of hydropower reservoirs. I think this can be a great advantage for us, at Cemig, opening up a range of potential applications as a support tool for decision in the Brazilian scenario of water modeling.

Thank you, Alberto, for this interview and all the best for this exciting PhD work!

Posted in decision making, forecast users, interviews, operational systems, seasonal prediction, water management | Leave a comment

Hydrological Forecasting at EGU 2018: time to write your abstract

You can contribute to advance hydrological predictions and forecasting systems through the presentation of your recent scientific developments, applications and approaches in the operation of hydrologic forecasting systems at the EGU Assembly in 2018.

Why should I go to EGU next year?

  1. Vienna is a beautiful city
  2. We all have a good time in the PICO and Poster sessions
  3. Hepexers always go out and it can be a lot of fun
  4. The oral sessions are a great opportunity to communicate my work
  5. I don’t know, but something tells me I should be there

Sessions

This year, the sub-division on Hydrological Forecasting, under the umbrella of the Division on Hydrological Sciences, is organizing seven sessions and co-organizing another two. Abstract submission is open until 10 January 2018, 13:00 CET:

Flash floods and associated hydro-geomorphic processes: observation, modelling and warning

[read more]

Convener: Isabelle Braud | Co-Conveners: Marcel Hürlimann, Marco Borga, Jonathan Gourley, Massimiliano Zappa, Jose Agustin Brena Naranjo
Predictive uncertainty estimation and data assimilation for hydrological forecasting and decision making

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Convener: Oldrich Rakovec  | Co-Conveners: Albrecht Weerts, Hamid Moradkhani, Marie-Amélie Boucher, Rodolfo Alvarado Montero, Joshua K. Roundy
Ensemble hydro-meteorological forecasting

[read more]

Convener: Fredrik Wetterhall  | Co-Conveners: Tomasz Niedzielski, Maria-Helena Ramos, Jan Verkade, Kolbjorn Engeland, Rebecca Emerton
Drought and water scarcity: monitoring, modelling and forecasting to improve hydro-meteorological risk management

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Convener: Brunella Bonaccorso  | Co-Conveners: Athanasios Loukas, Christel Prudhomme, Micha Werner, Carmelo Cammalleri
Operational forecasting and warning systems for natural hazards: challenges and innovation

[read more]

Convener: Michael Cranston  | Co-Conveners: Jan Szolgay, Ilias Pechlivanidis, Femke Davids
From sub-seasonal forecasting to climate projections: predicting hydrologic extremes and servicing water managers

[read more]

Convener: Louise Crochemore  | Co-Conveners: Henning Rust, Bart van den Hurk, Christopher White, Johannes Hunink, Tim aus der Beek, Louise Arnal
From probabilities to preparedness: early action in response to hazard forecasts

[read more]

Convener: Gabriela Guimarães Nobre  | Co-Conveners: Konstantinos Bischiniotis, Erin Coughlan de Perez, Brenden Jongman, Liz Stephens, Bart van den Hurk
Advances in statistical post-processing for deterministic and ensemble forecasts (main organization by NP Division)

[read more]

Convener: Stéphane Vannitsem  | Co-Conveners: Jakob W. Messner, Daniel S. Wilks
Uncertainty quantification in natural hazard and risk assessments: best practices and lessons learned across different hazards (main organization by NH Division)

[read more]

Convener: Paolo Frattini  | Co-Conveners: Daniela Molinari, Yoshiyuki Kaneda, Ivica Vilibic, Sergiy Vorogushyn

Check the full program of the Division on Hydrological Sciences for more sessions.

Deadlines

  • If you are eligible to apply for financial support and want to take this opportunity, you need to submit an abstract by 1 December 2017.
  • Otherwise, the deadline for abstract submission is 10 January 2018. Don’t miss it!

Detailed information on how to submit an abstract can be found here.

Posted in activities, announcements-events, meetings | Leave a comment

Why are meteorologists apprehensive of ensemble forecasts?

Contributed by Anders Persson, Uppsala, Sweden

A colleague in my world-wide meteorological network made me aware of a CALMet conference in Melbourne, i.e. dealing with meteorological education and training. Through the website you can access the program with more or less extensive abstracts. I have no doubt that most presentations were relevant and interesting, but what surprised me was that a search for the key words “probability” or “ensemble”  gave no hits. “Uncertainty” came up in only one (1) presentation, no 36 “To communicate forecast uncertainty by visualized product” by Jen-Wei Liu and Kuo-Chen Lu from the Central Weather Bureau in Taiwan.

This made me again ponder over the question why meteorologists still are apprehensive of ensemble systems (ENS) and probability forecasting.

1. Ensemble forecasting brings statistics into weather forecasting

Since the start of weather forecasting as we know it (in the 1860s), there has always been a rivalry between physical-dynamic-synoptic and statistical methods. Edward Lorenz’s famous 1959 experiment when he discovered the “butterfly effect” was part of a project in the late 1950’s to find out if statistical methods could be as effective in weather forecasting as numerical techniques. The answer was at the time not as clear-cut, but during the 1960’s, the numerical weather prediction (NWP) made much larger advances than the statistical approaches. Statistical methods were thereafter only used to calibrate NWP in what became known as MOS (model output statistics).

Over a lunch at ECMWF Edward Lorenz, on one of his annual visits in the 1990s, told us a parable he had got from the renowned Norwegian meteorologist Arnt Eliassen:

All the world’s birds wanted to compete who could fly the highest. They all set off ascending, but one after the other they had to drop out. Finally, only the great golden eagle was left. But as also he had to stop in order to return, a little sparrow who had been hiding in his feathers came out and managed to beat the eagle by a meter or two. The eagle is the dynamic NWP Eliassen had told Lorenz (who told us), the sparrow the statistical MOS.

To some extent the MOS can deal with uncertainties, but in a limited way since it is based on a deterministic forecast. It can estimate the general uncertainty at a certain range, but not distinguish between more or less predictable flow patterns. This is the strength, the core value of the ENS.

But ensemble forecasts are essentially statistical, probabilistic, and the meteorological education have always avoided to venture into this domain, except for those who wanted to become climatologists which in the old days was looked down upon. The ideal has been a physical-dynamic “Newtonian” approach, where perfect or almost perfect forecasts were seen as possible, if only the meteorological community got enough money to purchase better computers.

Indeed, it has paid of; the predictability range has increased by about one day per decade. Our five day deterministic forecasts today are as good and detailed as the two day forecasts in the 1980s. But also the demands and expectations from the public has increased. Even if we in a few decades from now can make more accurate and detailed seven day forecasts, there will still be questions about their reliability. The problem of uncertainty estimations will always be with us.

2. The ensemble system is a Bayesian system

But also among those meteorologists who are used to statistics, there is another problem. I became aware of that when I traveled on behalf of ECMWF to different Member States. A frequent question was: -How can you compute probabilities from those 50 members when you are not sure that they are equally likely?

My answer then was that we did not know! We did not know the likelihood of every member and we didn’t even know if they were all equally likely (probably they were not). But the verification statistics were good, and they would not have been so good if our assumption had been utterly wrong.

A typical “postage stamp map” from the ECMWF system. These 50 forecasts are not a priori equally likely, but since we do not know the probability of each of them we have to apply Laplace “Principle of insufficient reason” and assume that they are equally likely – an assumption which makes the system Bayesian. Image courtesy of ECMWF.

Only later I was made aware that my answer was the same as Siméon de Laplace had given two centuries earlier, when he was developing what is today known as “Bayesian statistics”: – We do not know, but make a qualified guess and see how it works out. Bayesian statistics, in contrast to traditional “frequentist” statistics, acknowledges the usefulness of subjective probabilities, degrees of belief. Laplace’s answer, which I unknowingly resorted to during my ECMWF days, is known as “Laplace principle of indifference”.

So part of the apprehension to ensemble forecasting cannot be attributed to ignorance, conservatism or “Newtonianism”, but has its basis in a long standing feud between “Bayesian” and “frequentist” statisticians. A “Bayesian” can look at the sky and say “there is a 20% risk of rain” whereas a frequentist would not dare to say that unless he had a diary which showed that in 34 cases out of 170 with similar sky, wind and pressure rain has occurred.

In recent years the gulf between “frequentist” and “Bayesians” has narrowed. Also, the calibration of the ENS data “à la MOS”, “washed away” much of the Bayesian characteristics and provided a more “frequentist” forecast product.

3. What is left for the forecaster?

Bayesian methods should not be alien to experienced weather forecasters. Since weather forecasting started in the 1860s there has been a strong Bayesian element in the routines, perhaps not described as such, but never the less this is how forecasters worked before the NWP. Who else but an experienced forecaster could look at the sky and give a probability estimate of rain? If the forecaster had a weather map to look at, the estimation would be even more accurate. Verification studies in the pre-NWP days in the 1950’s showed that forecasters had a good “intuitive” grasp of probabilities.

But with the advent of deterministic NWP the “unconscious” Bayesianism among weather forecasters evaporated gradually. The NWP could tell very confidently that in 72 hours time it would be +20.7 C, WSW 8.3 m/s and rain 12.4 mm within the coming six hours?

Anybody could read that information, your didn’t need to be a meteorologist. But you needed to be a meteorologist to have an opinion about the quality of the forecast: -Would it perhaps be cooler? The wind weaker? How likely is the rain?

There are currently more weather forecasters around than at any time before, in particular in the private sector where advising customers about their decision making is an important task (Photo from a training course at Meteo Group, Wageningen. Permission to use by Robert Muerau)

The risk was always that this forecast, even against the odds, would verify. So wasn’t it most tactical to accept the NWP? After all, if the forecast was wrong, the meteorologist had something to put his blame on. Some meteorologist took this easy road, but most tried to use their experience, knowledge of the models and meteorological know-how, to make a sensible modification of the NWP, including the reliability of the forecast. If the last NWP runs had been “jumpy” and/or there were large divergences among the available models.Tthis was taken as a sign of unreliability.

The “problem” for the weather forecasters was that with the arrival of the ENS they were deprived of even this chance to show their skill. The “problem” with a meteogram from ENS, compared to a more traditional deterministic from a NWP model, was that “anybody” could read the ENS meteogram! You didn’t need to be a meteorologist, not even a mathematically educated scientist. Einstein’s famous “grandma” could read the weather forecast and understand its reliability!

“You do not really understand something unless you can explain it to your grandmother.” – Albert Einstein

So what is left for the meteorologist?

I will stop here, because this text is already long enough. But the question above is really what educational and training seminars, conferences and workshops should be more focused on. I am personally convinced that the meteorologists have a role to play.

My conviction is based on my experiences from the hydrological forecast community, in particular the existence of this site. Is there any corresponding “Mepex“?

My conviction is also based on my experience as a forecaster myself, how the general public (and not so few scientists) need help to relate the uncertainty information to their decision making.

My conviction is finally based on my experiences from history that new tools always make traditional craftsmen more effective and prosperous – provided they are clever enough to see the new opportunities. Else they will miss the bus . . .

PS. To their credit it must be mentioned that EuMetCal is developing training resources for probabilistic forecasting. Ds.
All images from Thinkstock if not otherwise written.
Posted in ensemble techniques, forecast communication, opinion | 2 Comments

Ensemble prediction: past, present and future

Contributed by Fredrik Wetterhall and Roberto Buizza, ECMWF

The work of producing meteorological ensemble forecasts started 25 years ago at ECMWF and NCEP, and it sparked a revolution in both weather forecasts and its many applications. To celebrate this occasion, more than 100 people from across the world joined the 28 speakers at ECMWF’s Annual Seminar 11-14 September held in Reading, UK. The theme was “Ensemble prediction: past, present and future” and the four days where filled with presentations and discussions on what has been done, where we are and how we in the future can further improve the accuracy and reliability of ensemble-based forecasts.

Thanks to advances in models, data assimilation schemes and the methods used to simulate initial and model uncertainties, today ensembles are widely used to provide a reliable estimate of possible future scenarios. This is expressed for example in terms of probabilities of weather events or of risk indices. Increasingly, ensembles are routinely used to provide forecasters and users with the range of weather scenarios that could happen in the future. An example is given by the ECMWF ensemble-based strike probability of hurricane Irma, issued by ECMWF on 5 September.

The ECMWF ensemble-based strike probability that hurricane Irma would pass within a 120 km radius during the next 10 days, issued on the 5th of September (left panel).

Using ensemble forecasts

Different aspects of ensemble forecasting were discussed during the seminar, and they included the history and theory of ensemble forecasting, initial conditions, model uncertainties, error growth, predictability across scales, verification and diagnostics and future outlook. The full programme including recordings of the talks can be found here. The theme that may be of most interest for the HEPEX community was devoted to applications of ensemble forecasts. The session discussed the various ensemble products that now exist to help decision making (David Richardson, ECMWF), hydrological ensembles including the HEPEX experience (Hannah Cloke, Reading University) and observing and supporting the growing use of ensemble products (Renate Hagedorn, DWD). The session was testament as to how mainstream ensemble forecasts have become, not only in science but also in institutions and authorities that use probabilistic information in decision-making. There is still a lot to do to overcome some of the existing barriers, but the acceptance of ensemble forecast is truly a success story.

Panel discussions and looking forward

The seminar also included a panel discussion which provided an opportunity to explore and discuss in more detail some of the fundamental questions that are currently being tackled by the community, such as:

  • Should we be moving to small ensembles at high resolution, or large ensembles at more moderate resolution?
  • If the most cost-effective ensemble structure changes with lead time, should our ensemble be built so as to give a resolution and ensemble size that changes with lead time?
  • If an ideal ensemble consists of a set of equally likely members, is there a role for an unperturbed/central forecast?
  • What do we expect from the future in terms of our ability to represent model error in ensemble systems, and the representation of perturbations more generally?

It can be interesting to report some of the comments raised during the lively panel discussion:

  • Some users would react also at small probabilities: they would be the ones benefiting more from a size increase;
  • Ensemble size very is important both for the extended/long ranges and for high-resolution ensembles, to be able to capture the fine-scale details;
  • Considering the range of users of the ECMWF ensembles, overall, a size of 50 seems about right; although ECMWF principal aim should be to provide the best raw ensemble forecasts, it should work with the users to develop calibration methods, and understand whether the balance between ensemble size and resolution should be revisited once calibration methods are more widely used;
  • ECMWF should aim to provide the national meteorological services and its users with ensemble-based probabilistic forecasts that could be used by a wide range of users; it will be then up to the national meteorological services and/or third parties to design ‘tailored’ ensemble configurations that can address the needs of specific users;
  • We need more observation-based diagnostic to understand model error, and design better schemes;

Participants of the ECMWF Annual Seminar 2017. Photo: Simon Witter, ECMWF

The HEPEX community was an early advocator of using ensemble forecasts and it is important that we continue to push the boundaries of how ensembles should be used in research and applications. A good way of doing just that is to come to the HEPEX workshop in Melbourne next year!

Posted in activities, data assimilation, ensemble techniques, forecast techniques, forecast users, historical, meetings, operational systems, verification | 1 Comment

Final call for abstracts: 2018 HEPEX workshop in Melbourne, Australia

As you may have heard, the 2018 HEPEX workshop in Melbourne is coming up soon (Feb 6-8, 2018). Abstracts are due for submission on Sep 30, 2017. The workshop will feature both oral and poster presentations. The theme for the workshop is ‘breaking the barriers’ to highlight current challenges facing ensemble forecasting researchers and practitioners and how they can (and have!) been overcome. We wish to highlight the following barriers:

  • using ensemble forecasts to improve decisions in practice
  • extending forecasts in space (including to ungauged areas) and across lead-times, from short-term to sub-seasonal to seasonal forecast horizons
  • using ensemble forecasts to maximize economic returns from existing water infrastructure (e.g. reservoirs), even as inflows and demand for water change
  • using ensemble forecasts to improve environmental management of rivers
  • applying ensemble forecasts for agriculture
  • searching for better/new sources of forecast skill
  • balancing the use of dynamical climate and hydrological models with the need for reliable ensembles
  • communicating forecast quality and uncertainty to end users

More generally, we welcome contributions on new and improved ensemble hydrological prediction methods, as well the application of existing methods in practical and operational settings.

Keynote speakers for the workshop have been finalised – you can check out this and other information on the workshop website.

The HEPEX workshop is a highly effective forum for exchanging ideas and experiences on all things hydrological forecasting, and registration is free. So get to submitting those abstracts!

Any questions? Please contact us!

Posted in activities, announcements-events, meetings | Leave a comment

End-To-End Probabilistic Impact Based Early Warning Systems for Community Resilience

Contributed by Dr. Bapon Fakhruddin, New Zealand

Recently I attended the Fourth Pacific Meteorological Council (PMC) and Second Pacific Meteorological Ministers Meeting (PMMM), which was held in Honiara, Solomon Islands, from 14 to 17 August.

Figure 1. Dr Bapon Fakhruddin at the meeting. Photo credit: Jenny Davson-Galle

Reaching communities and ensuring that those most in need are provided with effective communications and technologies are top priorities for the Pacific Meteorological Council (PMC). Science has in-build uncertainty and is highly probabilistic. The probabilistic ensemble forecasting approach exposes the range of uncertainty associated with different predictions. They also allow the adoption of a risk-based approach for decision making and contribute to building confidence to help operational forecasters. Effective early warning systems (EWS) require a complete understanding of the populations and assets exposed to threats linked to the probabilistic ensembles forecasts.

With present extreme weather events, risk-based early warning systems are essential. Practice shows that people and communities at risk need to be involved in the understanding of their exposure and the vulnerabilities of different groups, including the disabled, the elderly, children and pregnant women. An effective system also relies on expert risk assessment, interpretation and communication. Currently, in many places, we do not use probabilistic risk assessment. Since science information is probabilistic, risk assessment needs to follow the same path.

Figure 2. Participants at the meeting. Photo credit: Jenny Davson-Galle

Understanding the forecast

Research has shown that, before deciding to take a disruptive – and often expensive – action such as evacuation, people must understand the forecast; they must believe that it actually applies to their situation and, most importantly, they must feel that they need to act because people, including their loved ones, are at risk. However, in many cases, common practice has been to prepare and release forecast messages without adequate concern on how they are received, understood and/or interpreted. Accurate, appropriate information that translates early warnings into early actions at community level is essential.

“We’ve always been talking about reaching the last mile, and that means getting to the people who haven’t got the message we are relaying. We’re talking about people with disabilities as well. They need to be included in our conversations and awareness efforts too,” said the Secretariat of the Pacific Regional Environment Programme’s (SPREP) Climate Change Advisor, Espen Ronneberg. “We do this to prepare those who are vulnerable to disasters as well, and that includes people with disabilities.” was added.

Acting on the risks

Disabled and elderly people are particularly at risk from natural disasters as, even with strong family and community support systems, it takes longer for them to reach designated safety zones. Likewise, extra forward planning is required for the evacuation of hospital patients and other health care facilities. Ronneberg believes that the way forward lays in encouraging disabled people to join in on EWS (Early Warning Systems) discussions. “I think the best way to include them would be through the People with Disabilities’ Forum, and it will be great if we can get them to take an interest in meteorology as well,” he said.

The PMC session included a discussion on new forms of risk assessment, such as the shift from deterministic to probabilistic risk estimation. Deterministic approaches are used to assess the impacts of a specific natural hazard scenario, whereas probabilistic methods are used to produce more refined estimates of how often a hazard is likely to happen, and the potential damage it will deliver, with the help of modelling tools. Probabilistic assessments work with uncertainties, partly due to the random nature of natural hazards, and partly because of our incomplete understanding of natural hazards and the limited ways of measuring hazards, exposure and vulnerability (OECD, 2012).

Communicating probability information

As hazard information is always probabilistic, the risk information and risk communication also need to be probabilistic. When any new probabilistic forecast product is introduced, it can be mis-communicated to the affected people. For people to make good decisions, the capacity to generate an early warning with an acceptable lead-time is essential. For example, advances in tropical cyclone (TC) forecasts using ensemble methods have been widely used for operational TC tracking. By using simple, weighted, or selective methods, TC tracking forecasts tend to have smaller positional errors than single model–based forecasts.

Figure 3 End-to-end early warning systems.

The impacts of climate variability and change were also recognised at the meeting as major challenges to island nations. Of particular concern to the Pacific region were sea level rise, salt water intrusion, drought, flooding, coastal inundation, ocean conditions (tides, swells, waves, acidification) and impacts on health (e.g. malaria and dengue), water resources, agriculture and fisheries (invasive species, etc.).

WMO’s approach to Climate Risk and Early Warning System (CREWS) initiatives and the requirements for disaster loss data standardization is crucial for impact-based early warning system, which offers more accurate risk assessment. See also a summary the outcomes of both the Multi-Hazard Early Warning System and the Disaster Risk Reduction Global Platform Meetings (http://www.wmo.int/earlywarnings2017/) held in May in Cancun, Mexico.

The next Pacific Meteorological Council (PMC) meeting will be held in Samoa in 2019. The PMC consists of members of the Pacific National Meteorological and Hydrological Services supported by its technical partners, regional organisations, non-government organisations and private sectors.


The 14-17 August meeting was co-hosted by the Government of Solomon Islands, the Secretariat of the Pacific Regional Environment Programme (SPREP) and World Meteorological Organisation (WMO).

 

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Quiz: Can you guess the city by looking at its river from space?

Contributed by Calum Baugh, Maria-Helena Ramos and Florian Pappenberger

Here are eight cities (and their rivers) seen from Google Earth. Can you recognize them?

Since nobody seems to have guessed the quiz we had in a previous post, we provide for each city/river some clues. Additionally, keep in mind the general clue for all of them: there is at least one Hepex member living in (or very close by) each of these cities (you don’t need to guess who they are…)

River 1: ‘Flooding often coincides with high tides in this city, its barrier can protect against storm surges…but for how long?’ Check the answer here

River 2: ‘The river drains the second largest lake in this country, industry thrived along its banks leading to the nickname Little Manchester.’ Check the answer here

River 3: ‘This river drains the lake which is eponymous from this city, flowing northwest, its steep gradient means there are 10 hydroelectric stations along its route prior to its confluence.’ Check the answer here

River 4: ‘Apparently, here, (non-hydrologists) tourists are often confused about the terms “right bank” and “left bank” and may spend hours trying to figure out which side of the river they are standing on.’ Check the answer here

River 5: ‘Heavy rain and catastrophic flooding was particularly observed in September 2013. They say the event went quickly from bad to worse. It was eight days, 1,000-year rain, 100-year flood.’ Check the answer here

River 6: ‘The canals somewhat dwarf the river here; perhaps hepexers will be more familiar with the beer to which the river lends its name.’ Check the answer here

River 7: ‘Flooding occurred along this river in May 2017 when nearly double the April rainfall average fell; however this city was spared any damage, with the worst affected being the cities upstream.’ Check the answer here

River 8: ‘Heavy flood protection prevents many floods in this city, but in 2014 the flooding resulted primarily from moderate rainfall combined with 111 km/h winds ‘pushing’ water upriver.’ Check the answer here

So, how many cities have you guessed right?

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Hydrologic similarity: Bridging the gap between hyper-resolution and hydrologic ensemble prediction

Contributed by:  Nate Chaney (Princeton University) and Andy Newman (NCAR)

The ever-increasing volume of global environmental data and the continual increase in computational power continue to drive a push towards fully distributed modeling of the hydrologic cycle at hyper-resolutions (10-100 meters) [Wood et al., 2011]. In principle, this has the potential to increase model fidelity and lead to more locally-relevant hydrologic predictions (e.g., soil moisture at the farm level).

However, for the foreseeable future, due to computational constraints this modeling approach will not be suitable for large ensemble frameworks—a pre-requisite for reliable operational applications given the unavoidable uncertainties in model structure, model parameters, and meteorological forcing.

However, this does not mean that hydrologic ensemble prediction should continue to rely on over-simplistic hydrologic models simply to maintain computational efficiency. It is undeniable that providing field-scale hydrologic predictions can have the potential to significantly advance the use of hydrologic models (e.g., precision agriculture).

Furthermore, the important role of the physical environment and human management in hydrologic response necessitates a more explicit representation of the spatial drivers of heterogeneity in hydrologic models. Therefore, there is a need for a modeling approach that can provide field-scale predictions while approximating the computational efficiency of existing hydrologic models used in ensemble frameworks. Contemporary applications of hydrologic similarity can satisfy both objectives.

Hydrologic similarity

Hydrologic similarity aims to harness the observed covariance between a system’s physical environment (topography, soil, land cover, and climate) and its hydrologic response to assemble robust reduced-order models.

Although originally limited to one-dimensional binning of over-simplistic metrics of hydrologic response (e.g., topographic index), recent advances have taken hydrologic similarity a step further; a system’s most representative hydrologic response units (HRUs) are defined by clustering the high dimensional environmental data space [Newman et al., 2014]—the petabytes of readily available high resolution global environmental data make this feasible over the globe. Semi-distributed models can then be built to simulate these HRUs and their spatial interactions (e.g., HydroBlocks, Chaney et al., 2016).

Within the clustered spatial domain, each fine-scale grid cell (~30 meters) is associated with a specific HRU through its environmental characteristics. This then makes it possible to map out the HRU simulations onto the fine-scale grid to approximate the fully distributed simulation (see Figure 1 for an example).

Using this approach, ongoing work continues to show that the fully distributed simulation can be closely reproduced using around 1/1000 of the number of HRUs; each grid cell is a unique HRU in the fully distributed simulation. In other words, the semi-distributed model can effectively provide the same hydrologic information as the hyper-resolution fully distributed model with only a fraction of the computation.

This equates to being able to run roughly 1000 ensemble members of the semi-distributed model in the time it would take to run the fully distributed model once; all while being assured that each ensemble member closely approximates its corresponding fully distributed solution.

In summary, contemporary implementations of hydrologic similarity provide a unique opportunity to bridge the gap between physically-based hyper-resolution modeling efforts and hydrologic ensemble prediction. It enables robust ensemble frameworks to provide locally-relevant information while ensuring they can robustly characterize the unavoidable uncertainties due to model structure, model parameters, and meteorological forcing.

References

  • Chaney, N., P. Metcalfe, and E. F. Wood (2016), HydroBlocks: A Field-scale Resolving Land Surface Model for Application Over Continental Extents, Hydrol. Process., doi:10.1002/hyp.10891.
  • Newman, A. J., M. P. Clark, A. Winstral, D. Marks, and M. Seyfried (2014), The Use of Similarity Concepts to Represent Subgrid Variability in Land Surface Models: Case Study in a Snowmelt-Dominated Watershed, J. Hydrometeorol., 15, 1717–1738.
  • Wood, E. F. et al. (2011), Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water, Water Resour. Res., 47(5).
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Quiz: Can you guess the river from the space?

Contributed by Calum Baugh, Maria-Helena Ramos and Florian Pappenberger

Here are four rivers seen from Google Earth. Can you recognize them?

River 1:

Check the answer here

River 2:

Check the answer here


River 3:

Check the answer here

River 4:

Check the answer here

 

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