The first online HEPEX game: try it yourself!

Contributed by Louise Arnal,University of Reading & ECMWF

On September 26, 2016, at the IMPREX (IMproving PRedictions and management of hydrological EXtremes; a Horizon 2020 project) General Assembly in Crete, around 50 participants took part in an online educational game called “How much are you willing to pay for a forecast?” designed by researchers from the University of Reading, ECMWF, Irstea and colleagues.

la-game-1This game was originally created as a paper game to be played during the Ensemble hydro-meteorological forecasting session at the EGU Assembly in 2015. It was designed to contribute to the understanding of the role of probabilistic forecasts in decision-making processes and to look at the perceived value of the forecasts by the decision-makers for flood protection mitigation.

This was achieved by giving the participants a set of probabilistic forecasts of their river level, with which they had to decide whether to buy flood protection. The participants’ willingness-to-pay for probabilistic forecasts was also evaluated during the game through an auction, where forecasts were no longer given but sold, and in a limited number.

First results of the game

The game was played in its paper version at several workshops and conferences after its introduction at the EGU session in 2015, from which a total of 129 game sheets were collected and analysed.

And just to mention a few key results,

  • the participants’ willingness-to-pay for a forecast showed that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.
  • Overall, the game has shown participants that the use and perceived benefit of probabilistic forecasts as a support of decision-making in a risk-based context is not straightforward.
  • This demonstrates that there is still an essential need to provide comprehensive guidance on the use of probabilistic information for decision-making, by communicating the quality as well as the actual relevance of the information for improved decisions for various applications.

If this short overview sparked your curiosity, a paper on the game and its results was recently published in a HESS special issue (Arnal et al., 2016).

An online version: try and play it yourself

The game was played actively but we thought that it could be better if it was adapted as an online game. So, here you have the first HEPEX online game!

It plays a little bit differently from the paper game, but I’ll let you discover that by yourselves. All you need to play it is a computer or smartphone, an internet connection and a valid email address. Do you want to try it?

The online version of the game was played at several conferences and workshops, such as the ‘Using ECMWF’s Forecasts (UEF)’ workshop held at ECMWF in June 2016 and the IMPREX GA in September 2016 (not to mention all the tests that our colleagues from ECMWF and Irstea kindly accepted to do before its release). Please bear in mind that as new ideas arise we make further developments and adjustments. So feel free to contact me or leave a comment below!

Games are increasingly promoted and used to convey information of scientific relevance. We believe that they are a great way to foster learning, dialogue and action through real-world decisions, which allow the study of the complexities hidden behind real-world decision-making in an entertaining and interactive set up.

Both the paper and the online versions of the game are available from the HEPEX website, where you can find several other games. Just download and try them for your teaching, meetings and training!

Posted in activities, decision making, floods, forecast users | 4 Comments

Ensembles of hydrological modellers can foster advances – Example with a participative workshop

Contributed by Guillaume Thirel, member of the IRSTEA Columnist Team

The 2013 IAHS General Assembly in Göteborg saw two very interesting events happening:

  • The biennal IAHS ultimate frisbee tournament, organised by Irstea since 2007 [1], that featured some of the Prediction in Ungauged Basins decade participants and some of the Hepex Blog columnists,
  • A participative workshop on modelling changing catchments, also organised by Irstea, that featured modellers willing to apply their beloved models under changing and challenging conditions.

These two topics, ultimate frisbee and participative workshops, represent some of the major focuses of our team: ultimate frisbee is played on a very regular basis at noon at Irstea or during conferences; we organised another participative workshop during the 2011 IAHS GA in Melbourne, in which a large panel of hydrologists was asked to analyse a set of hydrographs (see results by Crochemore et al., 2015 here); we regularly contribute to develop the HEPEX games you can find here; we recently participated in the multi-model exercise described by de Boer et al. (2016), among many other examples.

While posting about the frisbee tournament would be for sure much fun, I am going to post here about the participative workshop.


Figure 1: Hydrological modellers under changing conditions.

A workshop for modellers to confront their models to changing catchments

I was asked more than a year ago to post in this blog about this 2013 IAHS workshop, where we wanted modellers to confront their models to changing catchments. The rationale behind this is that we are often asked by water authorities to produce hydrological projections under IPCC scenarios, but in spite of knowing that models could have difficulties to deal with the very different future conditions (lack of robustness), we often tell ourselves and to others: “Come on, that will be fine, my model is good enough and physics is not going to change…”, without verifying much, or at all, the actual robustness of the model.

I must recognize that this topic may not be of direct relevance to what HEPEX is about, but several common points can be raised:

  • First, it is all about hydrological models (but simulation models rather than prediction models). They are of course at the centre of our research, regardless if we are forecasters or researchers in any other hydrological domain. For the workshop, we asked modellers to perform calibrations and simulations with their own models a couple of weeks before the workshop.
  • Second, we believe in collaborations to enhance the efforts of individuals (which is a strong common point with HEPEX). As a consequence, we organised this event as a real workshop, with lots of time dedicated to discussions and even working groups, instead of the usual presentation-only conferences format.
  • Third, we know the importance of modelling protocols. As recently stated in an HEPEX blog post, rigorous protocols are necessary to evaluate theories or algorithms. In the 2013 workshop, we defined a specific calibration protocol that each modeller had to apply to their model.
  • Fourth, we want to generalize our findings through the use of multiple catchments datasets. As large sample hydrology is now largely advocated, forecasting models and model developments for global changes purposes applied to multiple basins are now more and more common. In the workshop, we gathered a dataset of 14 catchments whose changes were precisely known and described.
  • Finally, many of HEPEX collaborators are also involved in model development and climate change applications and many improvements of hydrological models can benefit to hydrological forecasting.

To go into more details, we prepared for this workshop a dataset gathering lumped precipitation, temperature and discharge for 14 catchments. We also described the changes these catchments had faced (climate change, building of a dam, progressive or sudden land cover modifications). We asked the modellers to calibrate their models on six different periods: the complete period of available data, plus five equal-length subperiods, some of them being before the changes, some of them being after the change (Figure 2).


Figure 2 : The calibration protocol, where the models were calibrated on P1 to P5 periods and on the complete period (from a personal presentation at IUGG 2015).

We then proposed the modellers to send to me their simulations so that I could produce a large range of metrics and analysis plots (eg. Figure 3). The details about the protocol, metrics, plots and dataset can be found in Thirel et al. (2015).

By doing that, we provided the modellers with a good framework for:

  • analysing their models with common tools/methods,
  • assessing how the parameters and performances of models evolve with changes,
  • determining to which extent a model prepared before a change can provide realistic simulations after it,
  • proposing and evaluating solutions to improve the robustness of models.

Figure 3 : Evolution of the annual Nash criterion on low flows for a model calibrated on the six different periods. A dam sustaining low flows was built in the middle of the period. This shows that calibration is not sufficient to reproduce influenced low flows and a dam module would be necessary (after Thirel et al., 2015).

With more than 40 abstracts submitted, the outcomes from this workshop were numerous and it would be unfair to emphasize one specific work. The curious reader is invited to have a look at the Special Issue we organised in the Hydrological Sciences Journal and at the Editorial that summarises it (Thirel et al., 2015).

In my opinion, and I actually agree a lot with the outcomes from the Bertinoro workshop, common modelling protocols and datasets are absolutely necessary to better validate our advances in hydrological forecasting or hydrological modelling robustness. The combinations of these two elements allow for the reproducibility of studies, which is essential to serve as benchmarks to further advances.

[1] Check here for a history of all the IAHS games. The next game will take place in Port Elizabeth in July 2017 during next IAHS GA.

Posted in hydrologic models | Leave a comment

The importance of river hydrodynamics modeling for large scale flood forecasting

Contributed by Ayan Fleischmann and Fernando Fan, members of the LSH Research Group Guest Columnist Team

River hydrodynamics and hydrographs

Satisfactory flood predictions require satisfactory model representativeness of river transport processes. In the past years, the Large Scale Hydrology (LSH) Research Group has carried out many studies regarding the improvement of river hydrodynamics representation in large scale hydrological models (see the list of references at the end of this post) through the implementation of Saint-Venant or Local Inertial flow routing equations in rainfall-runoff models.

Indeed, transport processes are fundamental in defining the basin response (in other words, the basin outflow hydrograph). Hydrographs in steep terrains such as the Taquari-Antas tend to have a steep rising limb due to the non-linear relationship between flood wave celerity and discharge, while systems in flat terrains with well-developed floodplains tend to present attenuated and delayed peaks.

Also, hydrograph skewness is largely affected by how celerity interacts with discharge, so that in events that do not flood overbank, a positive skewed hydrograph tends to occur, while after floodplain inundation, negative skewed ones may happen (Figure 1). Such processes significantly alter peak travel times and the distribution of hydrograph volume, which are main outputs of flood forecasting models.


Figure 1. (i) Observed hydrographs for small and large floods in Piquiri River, Paraná river basin, and observed relationship between flood wave celerity and discharge for Piquiri River. (ii) Observed hydrographs in rivers with large floodplain influence in South America, showing negative skewness. Source: Fleischmann et al., 2016.

Sample cases

Most modeling applications of our group use the MGB-IPH hydrological model proposed by Collischonn et al. in 2007, which is a semi-distributed rainfall-runoff hydrological model developed for simulation of large basins. In the next paragraphs, we present some examples of the importance of correctly representing flood routing and river hydrodynamics for good model predictions in such rainfall-runoff models.

Our first example is from Paraná river basin, from the Itaipu Dam forecasting model (Pontes et al. 2015b, in Portuguese). In this study, the aim was to compare the use of the Local Inertial flow routing model using a simple floodplain storage model against the Muskingum-Cunge flood routing model, to accomplish flood forecasts (Figure 2). Both flow routing models are implemented in the MGB-IPH hydrological model. The case study is the Paraná River Basin, delimited until the Yacyretá reservoir (including Itaipu Dam). The assessments showed that MGB-IPH with Local Inertial model (red line) is better than the MGB-IPH using Muskingum-Cunge model (green line), in terms of diffusive effects and attenuation of floods. This is seen in Figure 2 because the red line (Local Inertial routing considering floodplains) is more similar to the blue line (observation) than the green one (Muskingum-Cunge model, without floodplains).


Figure 2. Observation (blue line), flow routing using Muskingum-Cunge (green line) flow routing using local Inertial model (red line) at (a) Porto São José gauge; (b) Itaipu and (c) R11-Monday gauge. Study area at the (d) Paraná basin.


Figure 3. Observed (blue) and simulated (red) discharges at União da Vitória gaugeQsim: simulated discharge. Qobs: observed discharge.

The second example is the Upper Iguaçu Basin. The Upper Iguaçu Basin suffers from frequent flooding, where the city of União da Vitória is located (see Araujo et al., 2014 for a description of flood forecasts in this area). Observed discharges at this city are shown in Figure 3, together with simulated flows with linear Muskingum-Cunge routing method. There is a clear smoothness and peak attenuation on observed hydrographs due to floodplains in upstream areas, which cannot be simulated with such simple routing methods.


Figure 4. (i) Comparison between simulated discharges with hydrodynamic and Muskingum-Cunge routing methods, with and without floodplains. Source: Paiva et al., 2013a. (ii) Observed (blue) and climatological (black) discharges and ensemble forecasts (grey) , for Solimões river, Amazon, at Manacapuru station. Source: Paiva et al., 2013b.

The third example is the Amazon river basin. Paiva et al. 2013 used MGB-IPH model to simulate the Amazon river basin, where extensive floodplains exist in the low, flat terrains of the central areas of the basin. Comparison of time delay index between Muskingum-Cunge routing method and hydrodynamics implementation (full Saint-Venant equations and representation of floodplain storage) showed around 60 days of peak time delay for simulations without floodplains (Fig 4.i).

Also, comparison between simulation with hydrodynamic model and Muskingum-Cunge with floodplains (Todini, 2007) indicate that other processes are also important for the definition of hydrograph shape, such as backwater and flood wave diffusion processes. Paiva et al. 2012 used this model to satisfactorily perform ensemble forecasts for the Amazon basin and evaluate the role of initial conditions on flood forecasting (Fig 4.ii). They showed that “uncertainty on initial conditions may play an important role for discharge forecasts even for large lead times (~1 to 3 months) on main Amazonian Rivers”, and that an “Ensemble Streamflow Prediction approach based on a hydrological model forced with historical meteorological data and using optimal initial conditions (e.g. data assimilation) may be feasible for hydrological forecasting even for large lead times (~1 to 3 months)”.

Finally, the fourth example is the Niger river basin model. A coupled hydrologic-hydrodynamic MGB-IPH model was also developed for the Upper Niger Basin (Fleischmann et al., paper in preparation), where a link between flooded areas and soil columns associated with hydrodynamic modeling of large scale floodplain channels allowed a good representation of flooded areas in the Niger Inner Delta as well as discharge and water level time series. Figures 5 and 6 present model results for Diré gauge station, downstream of Niger Inner Delta, with Muskingum-Cunge and Hydrodynamic flood routing methods.


Figure 5. Floodplain simulations of the Niger river basin. Blue color represents flooded areas.


Figure 6. Discharge simulations of the Niger river basin. Observation (blue line), flow routing using Muskingum-Cunge (green line) flow routing using local Inertial model (red line) at Diré gauge.

Implications for ensemble flood forecasting predictions

The representation of hydrodynamic processes is necessary for flood forecasting at  many large scale basins. We showed here some examples of watersheds modeled by our group in South America (Paraná, Iguaçu, Amazon) and Africa (Niger) where the correct modelling of floodplains is crucial for a good representation of the system. Flood peak magnitude depends mainly on (i) floodplain attenuation and (ii) acceleration due to non-linear relationship between flood celerity and discharge.

How this relates to ensemble forecasts? Let’s think about ensemble forecasting models in complex basins with floodplains, braided drainage network, or flat relief. Usually forecasts that incorporate more complicated hydrodynamics request longer simulation times and more complex models setup. This way, a number of forecasts, instead of a single one, would take more time than usual to run and the model needs to be very robust to support the various cases that can happen in terms of hydrodynamics. One of the aims of the research carried out by the LSH group in Brazil is to develop robust large scale models and efficient setups to couple with these situations.

Please let us know also about your experience with these situations in the comments!

See here the list of references.

Posted in columnist, floods, hydrologic models | Leave a comment

Crowdsourced data for flood hydrology – Interview with flood chasers in France and Argentina

Contributed by MH Ramos, member of the IRSTEA Columnist Team

A recent paper published in the Journal of Hydrology has drawn my attention: Crowdsourced data for flood hydrology: Feedback from recent citizen science projects in Argentina, France and New Zealand.


Using videos of flooding rivers to monitor floods (photo: FloodScale Project)

The paper deals with the use of information from social media in applied sciences and operations, with a focus on collecting photos and videos  to better assess river flows and to improve flood mapping after severe events. It prompts reflections on the way qualitative and quantitative data can be collected by the public and used in hydrology.

Three initiatives that have been held in France, Argentina and New Zealand are presented in details. From the paper, we learn the motivations and steps of implementation of each project: rules to follow, techniques, photo and video processing software, communication approaches to foster public participation and engagement, etc.

I had the opportunity to ask two questions to the main authors of the paper: Jérôme Le Coz, who is a colleague of mine from Irstea working in the centre located in Lyon, an expert on river hydraulics, morphodynamics and hydrometry, and Antoine Patalano, who I met in Argentina in 2015, when visiting the Universidad Nacional de Córdoba, where he is doing a PhD on large-scale, imaged-based techniques to quantify water surface velocity, under the supervision of Dr. Carlos Marcelo Garcia.

In their answers below, they explain why it is so exciting to chase floods and how it can contribute to enhance flood forecasting systems all over the world.

MHR: What is the most exciting thing about being a flood chaser?

Jérôme: Most field hydrologists are excited about chasing floods to measure them, and so are some non-professional enthusiasts of impressive hydroclimatic phenomena, usually to take beautiful pictures. I think chasing floods, especially flash floods, is similar to chasing storms, tornadoes or eruptions: you rush to be a privileged spectator of a remarkable display of Nature’s power.

Also, with our projects, we have realized that beyond the professionals and non-professional enthusiasts, any involuntary witness of a flood event who has a digital camera in hands can naturally become a flood chaser; it’s an increasingly natural reflex for many people.

Antoine: Being a flood chaser is exciting because you are participating in the characterization of an extreme event of Nature (e.g.: when chasing flash floods, where we have high flow velocities and a huge amount of water), and this by just using a cellphone or a digital camera that you control with your fingers. It’s also very nice to be part of a community that cares about science and environment.

MHR: How can this type of citizen science project contribute to improving real-time flood forecasting?

Antoine: The mountainous rivers of the Córdoba province, in Argentina, are characterized by the occurrence of flash floods. Due to the sudden nature of flash floods, it is very unlikely for a hydrologist to be able to record the peak discharge in river sections of interest. In case it is possible to get in time to the study sites, fast flow velocities and floating river debris can endanger both the instruments and the operators. Flash flood movies recorded by citizens can be processed to estimate river flow velocity and discharges using image velocimetry techniques. In most cases, the data obtained in this type of citizen science project is the only information available to characterize the observed hydrological events. Continuous records of digital cameras could be used in real-time data processing to update a forecasting model, for instance.

Jérôme: Collecting, processing and reviewing crowd-sourced hydrologic data in real time is more difficult than retrospectively. It was not done in our three projects but others were able to make the most of social media and smartphones for this, and populate real-time flood maps, for instance. Retrospective information obtained on flood processes can always be useful for improving real-time hydrologic modelling.

We can also consider the question the other way around: how could real-time flood forecasting help this type of citizen science project ? Some flood chasers already use weather and hydrological forecasts to be there where flooding will occur. Some chasers even manage to anticipate the forecasts, with an amazing intuition (or luck!). The higher the number of flood chasers, the higher the likelihood of obtaining good observations of flood events.

MHR: Thank you, Jérôme and Antoine, for your time and contribution! It is not explicitly said in this interview, but probably well understood by our readers, that care should be taken when doing “flood chasing” to not put your life or the lives of others at risk. Instructions can be found on the webpages of the projects mentioned in the paper (France: FloodScale and Argentina: Cazadores de crecidas).

There are many similar initiatives in other parts of the world. So, for those who are reading this blog post and have some additional thoughts and information on the experience of “flood chasing”, or links to flood videos to share, just leave a comment below.

Posted in columnist, data systems, flash floods, floods, monitoring | Leave a comment

Can a single hydrological model structure provide realistic simulations everywhere? Insights from the Bertinoro workshop

Contributed by Nans Addor and Gemma Coxon


View from the conference center on the rolling hills of the Emilia-Romagna region

In April 2016, thirty-one scientists and one mankini-clad Martyn Clark (check here for a recent HEPEX interview with M. Clark) ascended the steep hill on which the italian town of Bertinoro is perched to attend the workshop on ‘Improving the theoretical underpinnings of hydrologic models’.

The overarching aim of the workshop was to launch a new community initiative focused on developing a more structured and theoretically grounded approach to model development and testing (see Clark and co-authors published commentary).

As two of the early career scientists who participated in the workshop, we wanted to share our thoughts and views on what inspired us at the meeting with the wider community. Among the several outstanding questions that were addressed by the participants, three really caught our attention and are directly related to our ongoing research:

  • What are existing examples of hydrological theory and how do we develop new hydrological theory?
  • How can we reflect the structure of the landscape in the structure of the models?
  • How can we develop rigorous approaches to evaluate and select among competing theories and algorithms in the presence of highly uncertain observation data?

After the workshop, the two of us went back to these questions, and revisited them by combining key messages distilled during the workshop together with ideas based on our own experience and research interests. Here we present the results of our post-workshop reflections by focusing on a specific topic: the suitability of hydrological models across varying environments.


Fresco in the main conference room

Our post-workshop reflections

There is a growing number of studies in which a single model structure is applied over large regions and across very diverse hydro-climatic conditions. The HBV model (see also the 2014 Hepex blog post interview with its father, Sten Bergström), for instance, has been run at the global scale (Beck et al, 2016) and models like VIC or mHM are routinely run over continental domains (e.g. Mizukami et al., 2015; Rakovec et al., 2016).

This underscores model flexibility and the considerable progress made recently in estimating parameters over large domains. Consequently, it is now easier than ever to apply a hydrological model everywhere, however, this does not imply model realism. Since model structures have been stretched and run over large regions, attention now needs to be focused on the question, “is the realism of the resulting simulations satisfying”?

In this context, a crucial hypothesis, on which many large-sample modelling experiments and large-scale modelling experiments rest is that: “A single hydrological model structure can provide realistic simulations everywhere”

It is our impression that this hypothesis deserves particular attention and that there are diverging opinions in the community on its validity. In a nutshell, if we can falsify this hypothesis, then parameter flexibility alone is not sufficient, and new methods are necessary to guide model structure choices based on the attributes of the environment. To illustrate this, we approach this hypothesis from the perspective of two presentations held at the workshop by Fabrizio Fenicia and Luis Samaniego. Clearly, this is part of a wider discussion within the hydrological community, but in this post based on the workshop, we are comparing the approaches of two participants. Our aim is to stimulate a dialogue on how to best approach and explore the crucial hypothesis highlighted above.

Two approaches from two presentations

  • A significant part of Fabrizio Fenicia’s recent work relies on building and testing modular modelling frameworks. The underlying idea is that the differences between different environments should be represented by different model structures (e.g. Fenicia et al., 2014). An advantage is that relationships between dominant hydrological processes and model structure are explicit, since adequate models are chosen based on the understanding of catchment behaviour, which significantly relies on expert knowledge and field experimentations. This however makes model selection difficult to automatize, especially over large areas, since available datasets do not provide the same support to discriminate between competing hypotheses on hydrological behaviour than personal experience. Furthermore, the use of different models over space makes large-scale modelling difficult.
  • In contrast, Luis Samaniego’s recent work based on the multiscale parameter regionalization (MPR, Samaniego et al., 2010) technique and the mHM model builds on the idea that a single model structure can be applied at the continental scale, and adjusted to specific environments by changing its parameter values. Key advantages of this approach is that i) it yields continuous parameter fields with realistic spatial features and ii) it guarantees consistency across spatial scales. Yet the first advantage is a direct result of the use of realistic catchment attribute maps (e.g. soil maps) to produce parameter maps, so in itself is no proof that hydrological processes (e.g. water percolation) are realistically captured. The second advantage is also obtained by construction, this time using the optimization of an objective function to force consistency between water fluxes at different spatial scales (e.g. Rakovec et al., 2016). Nevertheless, we see these two advantages as very likely to contribute to more realistic simulations, and consider them as essential to progress with parameter estimation at the continental scale. Furthermore, MPR opens new opportunities by allowing for the integration of recent (and future) data sets (e.g. FLUXNET and GRACE) to constrain parameter values, and thereby goes beyond traditional approaches based on discharge alone.

We believe it is crucial that both avenues are actively pursued. As long as we can’t falsify the hypothesis that “A single hydrological model structure can provide realistic simulations everywhere”, we still need to develop new methods for parameter estimation, particularly when we need to ensure spatial consistency.

Yet questions still remain over which models are best suited to be applied everywhere and so continued efforts need to be made on testing different model structures across a broad range of environments, and establishing which model processes are missing in current hydrological model structures where we are unable to achieve good model simulations.

To achieve this, we are both involved in research projects in which both approaches are combined, i.e. modular modelling frameworks and novel parameter estimation methods. Our objective is to get a better sense of when it is really necessary to vary the model structure: experience has shown that good (and even realistic) simulations can be achieved by only varying the model parameters, making clear that it is not necessary to have a different model structure everywhere. Yet it is not entirely clear how much a single model structure can be stretched, i.e. in which circumstances it stops providing realistic simulations.

We believe that hydrological modeling experiments in a large number of catchments can provide new insights in this respect and help us to determine how much flexibility should be provided by the model structure and model parameters, respectively.


  • Beck, H. E., van Dijk, A. I. J. M., Roo, A. de, Miralles, D. G., McVicar, T. R., Schellekens, J. and Bruijnzeel, L. A.: Global-scale regionalization of hydrologic model parameters, Water Resour. Res., 52(5), 3599–3622, doi:10.1002/2015WR018247, 2016.
  • Clark, M. P., Schaefli, B., Schymanski, S. J., Samaniego, L., Luce, C. H., Jackson, B. M., Freer, J. E., Arnold, J. R., Moore, R. D., Istanbulluoglu, E. and Ceola, S.: Improving the theoretical underpinnings of process-based hydrologic models, Water Resour. Res., 52(3), 2350–2365, doi:10.1002/2015WR017910, 2016.
  • Fenicia, F., Kavetski, D., Savenije, H. H. G., Clark, M. P., Schoups, G., Pfister, L. and Freer, J.: Catchment properties, function, and conceptual model representation: is there a correspondence?, Hydrol. Process., 28(4), 2451–2467, doi:10.1002/hyp.9726, 2014.
  • Mizukami, N., Clark, M. P., Gutmann, E. D., Mendoza, P. A., Newman, A. J., Nijssen, B., Livneh, B., Hay, L. E., Arnold, J. R. and Brekke, L. D.: Implications of the Methodological Choices for Hydrologic Portrayals of Climate Change over the Contiguous United States: Statistically Downscaled Forcing Data and Hydrologic Models, J. Hydrometeorol., 17, 73–98, doi:10.1175/JHM-D-14-0187.1, 2015.
  • Rakovec, O., Kumar, R., Mai, J., Cuntz, M., Thober, S., Zink, M., Attinger, S., Schäfer, D., Schrön, M. and Samaniego, L.: Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins, J. Hydrometeorol., 17, 287–307, doi:10.1175/JHM-D-15-0054.1, 2016.
  • Samaniego, L., Kumar, R. and Attinger, S.: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46(5), doi:10.1029/2008WR007327, 2010.

Other HEPEX blog posts on hydrological models can be seen here.

Posted in hydrologic models, multimodel | 1 Comment

High-resolution flood forecasting in Sweden: a status update

Contributed by Jonas Olsson (SMHI), member of the SMHI Guest Columnist Team

Traditionally, hydrological activities (observations, modelling, forecasting) at SMHI have mainly focused on Sweden’s large rivers. The largest ones are Göta River with a catchment size of ~50 000 km² and Torne River with ~40 000 km² and then there are many (often regulated) with a catchment size of 20 000 to 30 000 km². The HBV model in combination with comparatively coarse-scale geographical and (in time and space) meteorological data has worked excellently for all forecasting purposes.

However, some five years ago technical advances as well as societal needs spawned an initiative to work towards higher-resolution flood forecasting. On one hand the national set-up of the HYPE model (Lindström et al., 2010) – S-HYPE – was under development. In S-HYPE Sweden was initially divided into ~17 000 sub-catchments (mean size ~25 km²), making it possible to work with also small and fast-responding catchments. On the other hand the issue of cloudburst consequences in terms of urban flooding, debris flow, etc., received a growing attention, not least following the severe flooding in Copenhagen in July 2011, which could equally well have hit Sweden. The time step used in simulation and forecasting was 1 day, which meant for example that the potential gain of the high spatial resolution was not realised and that forecasters had limited possibilities to predict and follow fast floods caused by cloudbursts.

So, we decided to start developing S-HYPE (which is today the basis of the forecasting system, now with almost 40 000 sub-catchments) for running at shorter time steps, with 1 hour as a target value. A project proposal was submitted to the Swedish Civil Contingencies Agency (MSB) and it was granted – thank you! – and this project ended last year. In the following I give an overview of the main activities included.

Precipitation forcing data

To run S-HYPE with a 1-hour time step we first of all needed historical meteorological forcing on the national scale with a 1-hour time step and a sufficiently high spatial resolution. This was obtained by starting from radar-observed precipitation from the Baltrad network ( and apply a bias-correction step against gridded station data to ensure bias-free long-term accumulations (Berg et al., 2016). The resulting data set – HIPRAD (HIgh-resolution Precipitation from gauge-adjusted weather RADar) – has a 15-min time step, a 2×2 km² spatial resolution and goes back to 2000.

S-HYPE parameters and calibration

The parameters were divided into three categories: 1) time-step independent, 2) linearly scalable with time step, and 3) requiring re-calibration. An initial calibration was performed for selected catchments that had recently experienced flooding and then a more general optimization was performed for southern Sweden. Figure 1 shows that NSE is generally above 0.7 which we consider quite OK as a start.

Figure 1. Performance in terms of NSE of the first version of 1-hour S-HYPE in discharge stations in southern Sweden (circle size represents catchment size).

Figure 1. Performance in terms of NSE of the first version of 1-hour S-HYPE in discharge stations in southern Sweden (circle size represents catchment size).

Precipitation forecasts

The development of high-resolution meteorological forecasts is very fast and there is always something better “just around the corner”, which naturally reduces ones motivation to evaluate the existing products. But we made an effort to evaluate nowcasts from SMHIs KNEP system (Ridal et al., 2011) based on the question can a short-duration 10-year rainfall be at all predicted? A total of 48 observed events with a duration between 1 and 24 hours and a mean return period of 10 years were identified, characterised and evaluated (Olsson et al., 2014). As expected predictability was limited, but for short lead times (<3 hours) there were some if allowing some error margin in time (we hydrologists can quite often live with some temporal error) and space (we hate spatial errors).

A lot of further development and evaluation certainly remains but there is now a first version a 1-hour S-HYPE running in pre-operational forecast mode, continuously initialised by a real-time version of HIPRAD and forced with different high-resolution meteorological forecasts. Unfortunately the forecasts are not yet available to the forecasters on duty due to technical complications (always these technical complications…) but we hope to have that solved soon (fortunately this summer was rather cloudburst-free in Sweden).

Finally let me briefly describe some related ongoing activities in order to make everything better in different respects.

Urban areas

In the original, 1-day S-HYPE the description of urban areas is approximate, both in terms of land-use (coming from EEA CORINE) and parameterisation. An improved urban land-use class in HYPE has been developed and more detailed land-use data, for example from the EEA Urban Atlas, is being explored.

Rainfall visualisation

There is clear scope for improving how observed short-duration rainfall is visualised, to support the hydrological forecaster. Currently there is only either hourly values from the station network (Figure 2a), with a lot of unobserved space between the stations, or animated sequences from the radar (Figure 2b), which are very hard to interpret in terms of local accumulations. Figure 2c shows a mock-up of a tool in which catchment-scale accumulations (for a selected historical period/duration) can be visualised in terms of their estimated return period.

Figure 2. Rainfall observations as visualised using station data (a), radar sequences (b) and in the tool under development (c).

Ensemble forecasts

Of course I cannot write in the HEPEX blog without mentioning ensemble forecasts. We are currently looking at coupling 1-hour S-HYPE with 11-member ensembles (1-hour time steps 36 hours ahead, 2.5×2.5 km² grid) from the HarmonEPS system based on the AROME model (Seity et al., 2010). To practically and meaningfully use these high-resolution ensembles with a national perspective will be a challenge – and we like challenges!



Berg, P., Norin, L., and J. Olsson (2016) Creation of a high resolution precipitation data set by merging gridded gauge data and radar observations for Sweden, J. Hydrol., in press, doi:10.1016/j.jhydrol.2015.11.031.

Lindström, G., Pers, C., Rosberg, J., Strömqvist, J., Arheimer, B., 2010. Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrol. Res., 41, 295–319, doi:10.2166/nh.2010.007

Olsson, J., Simonsson, L., and M. Ridal (2014) Rainfall nowcasting: predictability of short-term extremes in Sweden, Urban Water J., 11, doi: 10.1080/1573062X.2013.847465.

Ridal, M., Lindskog, M., Gustafsson, N., and Haase, G., 2011. Optimized advection of radar reflectivities. Atmospheric Research, 100, 213-225.

Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier, F., Lac, C., Masson, V., 2011. 631 The AROME-France convective scale operational model. Mon. Weather Rev. 139, 976-999, doi: 632 10.1175/2010MWR3425.1

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Making connections through HEPEX – A CSIRO experience

By QJ Wang, Andrew Schepen, David Robertson, James Bennett, Durga Lal Shrestha, Yong Song and Tony Zhao members of the CSIRO Columnist Team

HEPEX has a reputation as a strong community of like-minded, supportive researchers and practitioners who are collectively bent on significantly advancing the science and applications of ensemble hydrological forecasting. HEPEX provides a unique forum where meeting people and exchanging ideas has the very real potential to open up new, exciting collaborative research opportunities. Indeed, this has been the experience of the CSIRO water forecasting team. A number of joint research activities have been kick-started in the wake of HEPEX workshops.


Participants in the last HEPEX workshop in Quebec, Canada, in June 2016

Following the Beijing workshop in 2012, Florian Pappenberger from ECMWF and our team saw an opportunity to work together in applying our CBaM rainfall post-processing method to seasonal forecasts from ECMWF’s System4 global model. A PhD student visiting CSIRO subsequently evaluated CBaM and System4 for seasonal climate forecasting across the whole of China. This led to a publication in JGR: Atmospheres.

A relationship with the NCEP Climate Prediction Center (CPC) was seeded during the HEPEX workshop in Maryland in 2014. The well-connected HEPEXers Andy Wood, Qingyun Duan and D-J Seo kindly introduced QJ Wang to colleagues in the CPC. This led to a number of side meetings at the time and many follow-up communications in the subsequent two years. Now CPC has a two-year funded collaborative project with our team to apply CBaM to CFSv2 and NMME data for US seasonal climate forecasting.


QJ Wang (CSIRO) presenting at the HEPEX Workshop on seasonal hydrological forecasting in Norrköping in September 2015

The SMHI Norrkoping workshop was similarly fruitful. We are currently working with Michael Butts and his team at DHI on an initial evaluation of methods for seasonal hydrological forecasting in Europe. From presentations during the same workshop, it was clear that the method of quantile-mapping (QM) had become very popular for post-processing ensemble seasonal climate forecasts. Following discussions with Andy Wood and Maria-Helena Ramos at the workshop, we decided to conduct an evaluation of QM to highlight its strength and weakness. This has now resulted in a joint research paper, which is currently in review for possible journal publication.


Andrew Schepen (CSIRO) presenting the HEPEX seasonal streamflow intercomparison project at the HEPEX Workshop in Quebec in June 2016

Of course, there is also the HEPEX seasonal ensemble hydrological forecasting test-bed initiative, in which we are actively participating.

Regionally our team is also reaching out to connect to forecasting researchers and practitioners in Asia through HEPEX special sessions at conferences like AOGS and MODSIM.

These are examples of connections that we, the CSIRO water forecasting team, made through the HEPEX forum.

HEPEX is sometimes said to have a friendly, big-family atmosphere and there is no doubt that others have been making many connections too. We certainly encourage all to capitalise on opportunities to make new connections and advance our science.

Who have you met and what are you working on? We’d love to hear your stories in the comments.

Posted in columnist, meetings, projects | 1 Comment

The quest for better global precipitation data

Contributed by Hylke Beck, Albert van Dijk, Ad de Roo, Jaap Schellekens, Diego Miralles, Brecht Martens, and Vincenzo Levizzani

Information on precipitation is essential for almost any hydrological study. Unfortunately, precipitation is also one of the most difficult to estimate meteorological variables, due its tremendous spatio-temporal heterogeneity, particularly in tropical, mountainous, and snow-dominated regions.

Over the past decades, many precipitation datasets have been developed using different techniques and observation sources (see, for instance, International Precipitation Working Group, UCAR Climate Data Guide, and

So, which precipitation dataset should one use?

Well, that depends on the application, but in the context of global hydrological modelling it has never really been possible to identify the “best” dataset for several reasons.

  • Importantly, none of the currently available datasets exploit the complementary nature of satellite and reanalysis data, or fully use all the available gauge data.
  • Moreover, many datasets cover only part of the global land surface, and have a short temporal record, or a coarse spatial or temporal resolution.
  • Last but not least, the datasets rarely fully account for gauge undercatch and orographic effects, and therefore exhibit biases in snow-dominated and mountainous areas (see, for example, Herold et al., 2015).

The MSWEP dataset

GPD-Fig1To address these shortcomings, we recently developed a new global precipitation dataset, with higher spatial and temporal resolution than most available datasets (0.25° and three-hourly, respectively). Referred to as Multi-Source Weighted-Ensemble Precipitation (MSWEP; fully described in Beck et al., 2016), the dataset currently covers the period 1979–2015.

It is unique in that it uses an unprecedented range of data sources, including two datasets based solely on rain gauges (GPCC and CPC Unified), three on satellites (CMORPH, GSMaP, and TMPA), and two on reanalyses (ERA-Interim and JRA-55). The use of rain-gauge data ensures reliable precipitation estimates in regions with dense rain-gauge networks, while satellite and reanalysis data are used to enhance the precipitation estimates in sparsely-sampled tropical and snow-dominated regions.

Another useful aspect of MSWEP is that it accounts for gauge undercatch and orographic effects in a new way. Traditionally, equations that incorporate wind speed and air temperature have been used to correct for gauge undercatch, but we did not find these equations to perform well. In Central Russia, for example, precipitation was consistently overestimated, while there was insufficient precipitation in many mountainous regions. Instead, we developed a novel approach to infer the “true” precipitation from streamflow observations for ~14,000 catchments across the globe using a Budyko-type equation, after which we interpolated the resulting bias correction factors to obtain a global bias correction map (see figure below).


Figure 1: Bias correction factors based on streamflow observations.

The video below shows an animation of three-hourly precipitation data from MSWEP for the seven-month period of June to December 2006.


Figure 2: Three-hourly MSWEP precipitation, for the seven-month period from June to December 2006 [Click on the image to see the animation].

The patchy nature of precipitation in the tropics, where MSWEP relies mainly on data from instruments onboard the (now decommissioned) TRMM satellite, is due to the localised convective storms that develop in warm, moist tropical conditions.

By contrast, large frontal systems occur when cold and warm air masses collide in poleward regions, where MSWEP relies primarily on reanalysis datasets produced using atmospheric models, which are designed to simulate large-scale weather systems. Finally, large mountain ranges, for example along the Pacific Coast of North America, show the occurrence of orographic storms due to the uplift of moist air.

MSWEP is freely available via For more information, see our open-access publication in HESS Discussions here.

Applicability of MSWEP

MSWEP is useful for a broad range of hydrological applications from local to global scales.

  • The value of MSWEP for streamflow simulation has been assessed by Beck et al. (2016). To this end, we calibrated the popular HBV hydrological model against daily streamflow observations with different precipitation estimates, including MSWEP. The calibration was performed for 9,172 catchments across the globe.

Figure 3: Median NSE scores obtained after calibration of HBV using different precipitation datasets. Catchments are grouped on the y-axis according to mean distance to the closest rain gauge.

The figure on the right presents median Nash-Sutcliffe Efficiency (NSE) scores obtained after calibration for catchments grouped according to their mean distance to the closest rain gauge. MSWEP (dark blue) obtained higher scores for all groups, suggesting that MSWEP performs well in regions with dense as well as sparse rain-gauge networks.

The results for catchments with a mean distance of more than 25 km to the closest rain gauge are of particular interest, since ~84% of the global land surface falls into those categories!

  • The value of MSWEP for evaporation and soil moisture simulation was also assessed. For this purpose we used the Global Land Evaporation Amsterdam Model (GLEAM), a relatively simple, observation-driven model designed to estimate terrestrial evaporation from satellite-based inputs.

For 64 FLUXNET tower stations around the globe, the GLEAM-based evaporation and soil moisture simulations obtained using precipitation from MSWEP and CPC Unified (which is based on rain-gauge data) were validated against observations. MSWEP was found to perform at least as well as CPC Unified, even though FLUXNET tower stations are concentrated in regions with dense rain-gauge networks, where the quality of CPC Unified is known to be very high.

Based on these results, and on the perceived higher quality of MSWEP in sparsely-gauged regions, MSWEP has been  selected as the core precipitation forcing for the GLEAM v3.0a dataset, a global 0.25° 35-year record of terrestrial evaporation and soil moisture (freely available here). See the paper by Martens et al. (2016) for more details on the GLEAM-derived datasets.

What’s next?

We plan to continue updating the MSWEP estimates to take advantage of new and improved data sources such as ERA5 (the successor of ERA-Interim; see this presentation by Dick Dee from ECMWF), IMERG (the successor of TMPA; more details and near real-time precipitation maps available here), and SM2RAIN (a precipitation product derived from satellite-based surface soil moisture; see Brocca et al., 2014).

We also plan to include ocean areas, to make MSWEP a truly global dataset, and to correct the daily precipitation distribution data calculated using gauges – mainly to reduce the “drizzle effect” (explained in Maraun, 2013).

Finally, we are developing a near real-time (NRT) version of MSWEP. This product, called MSWEP-NRT, would be the first NRT product to provide fully global precipitation estimates with demonstrated good performance in densely gauged areas as well as tropical and polar regions. MSWEP-NRT should be particularly useful to improve the initialization of the model state variables in global or regional forecasting systems, that is, the amount of water stored in the snowpack, the soil, the groundwater, and other water stores. The Global Flood Awareness System (GloFAS), for example, currently uses the 24-h lead time ECMWF forecast from yesterday to initialize the forecast of today, which means that initialization is solely based on weather model data. The use of MSWEP-NRT should improve initialization in densely gauged regions by taking advantage of the rain gauge data, and in tropical regions by taking advantage of the satellite data. That way, better estimates of precipitation will help achieve better streamflow forecasts.


Posted in data systems | 5 Comments

The Current State of Operational Global (and continental) Scale Flood Forecasting

contributed by Rebecca Emerton, PhD Student at the University of Reading and ECMWF

After reading Chantal’s recent blog post discussing some of the limitations for international flood forecasting, and last week’s interview with Thomas Adams and Tom Pagano discussing their new book describing national and regional flood forecasting systems around the world, I thought it would be interesting to add to this dialogue with a brief overview of the current state of large-scale (global and continental) operational flood forecasting.

It has only really become possible to produce flood forecasts at the global scale in recent years, largely due to the integration of meteorological and hydrological modelling capabilities, improvements in data, satellite observations and land-surface hydrology modelling, and increased resources and computer power. Two of the key scientific and technological advances that have enabled the forecasting of floods at the continental and global scale include the significant increase in the skill of precipitation forecasts in recent years, and the move from deterministic to probabilistic (ensemble) numerical weather prediction (NWP).

Key factors

Three key factors highlight the need for large-scale, international flood forecasting systems:

  1. to provide information on floodiness (Stephens et al., 2016) across areas larger than a catchment, for example, to indicate where flooding during the rainy season will be worse than normal; information that is of high importance to humanitarian organisations (Braman et al., 2013)

  2. to provide forecasts in basins across the globe where there are currently no forecasts available, aiding disaster risk reduction and flood early warning efforts globally

  3. to support existing capabilities, for example, by using ensemble forecasting techniques to enable probabilistic flood forecasts, or at longer lead times for earlier warnings, as these can be computationally expensive to run.

At present, these forecasting systems are able to produce coarse-scale discharge forecasts covering entire continents or the globe. As previously discussed by Chantal, Thomas and Tom, despite recent advances, there are several grand challenges faced by international forecasting systems at present and moving forward, from data availability to resources and effective communication of forecasts.

At present, there are four operational continental scale flood forecasting systems, and two global scale. Below, I have given a very brief summary of each. These systems are continuously evolving, and are already proving to be key in supplementing national and local forecasting capabilities while supporting global-scale activities. Thanks to my co-authors at the centres running each of these systems, you can read more about each system, such as the components of the model and their operational uses, and the current state of large-scale flood forecasting (including the advances that have led to where we are now, the grand challenges facing the community, and future directions), here: Continental and Global Scale Flood Forecasting Sytems (Emerton et al., 2016). For anyone interested in reading further around national and regional forecasting systems and their histories, I also include a link to the “Flood Forecasting, A Global Perspective” book discussed in last week’s post.

The European Flood Awareness System (EFAS)

EFAS has been fully operational since 2012, with the aim of providing transnational, harmonised early warnings of flood events and hydrological information to national agencies, complementing local services. Four different meteorological forecasts are used as input to the Lisflood hydrological model, producing simulated ensemble hydrographs; however, these alone do not constitute a flood forecast – a decision-making element needs to be incorporated (Thielen et al., 2009). As such, historical meteorological data are run through Lisflood to calculate a 22-year time series of river flow, providing reference thresholds for minor or major flooding. EFAS sends warning emails to national authorities responsible for flood forecasting across Europe, and daily overviews are sent to the Emergency Response Coordination Centre (ERCC) of the European Commission.

European Hydrological Predictions for the Environment (E-HYPE)

H:\Review_Paper\Long Version\ehype_interface.pngE-HYPE is a multipurpose model based on open data, which is used for various applications such as water management, research experiments and flood forecasts. The E-HYPE Water in Europe Today (WET) tool compares the current hydrological situation with climatological data and past modelled events. E-HYPE currently uses a single deterministic NWP input, although ensemble forecasting is intended for future system developments. Alongside natural processes, HYPE also takes into account anthropogenic influences including irrigation and hydropower.

The BoM Flood Forecasting and Warning System

H:\Review_Paper\Long Version\bom_graphics.png

The BoM has been producing flood forecasts operationally for several decades, with the technology and systems used to produce these forecasts continually evolving. Recently, short-term continuous streamflow forecasting has been introduced, alongside event-based hydrological modelling and nowcasting using radar rainfall estimates. The BoM system uses various resolutions (each covering a different spatial scale) of their own deterministic NWP model as input to the Génie Rural á 4 Paramètres (GR4) hydrological models, and forecasters are also able to produce “What If” precipitation scenarios to force the hydrological models. For event-based forecasts, local models are used in support of the continental scale system.

The U.S. Hydrologic Ensemble Forecast Service (HEFS)

H:\Review_Paper\Long Version\hefs_screenshot_2(locationsinflood)_copy.png

The HEFS has been running experimentally at several regional River Forecast Centres (RFCs) across the U.S. since 2012, and is currently being rolled out operationally at all 13 RFCs. The HEFS aims to produce ensemble streamflow forecasts that seamlessly span lead times from less than 1 hour up to several  years, and that are spatially and temporally consistent, calibrated and verified. The HEFS takes four meteorological inputs; two NWP models, alongside the RFC forecasts and historical observations, and each RFC may use a different combination of 19 components within the Hydrologic Processor suite (which contains various hydrologic, hydraulic, reservoir and routing models). Also included in the HEFS are pre- and post-processing steps which allow both the uncertainties in the meteorological input and the hydrology to be taken into account.

The Global Flood Awareness System (GloFAS)

H:\Review_Paper\Long Version\glofas_interface.png

GloFAS (Alfieri et al., 2013) has been producing probabilistic flood forecasts for the global river network with up to 2 weeks lead time in a pre-operational environment since 2011, and can provide downstream countries with early warnings and information on upstream river conditions alongside global overviews of upcoming flood events in large river basins. GloFAS takes the surface and sub-surface runoff from the ECWMF ensemble prediction system as input to the Lisflood river routing model, with runoff from the ERA-Interim reanalysis archive also run through Lisflood, in order to compute return periods for the global river network. Unlike EFAS, GloFAS does not directly disseminate flood warnings, as each country has national procedures to follow, but anyone is able to access and analyse the forecasts for decision-making purposes and research.

The Global Flood Forecasting Information System (GLOFFIS)

H:\Review_Paper\Long Version\gloffis_vis.png

GLOFFIS is a research-oriented operational system based on Delft-FEWS (56), and similarly to several of the continental-scale systems, uses several meteorological inputs to drive two hydrological models; PCR-GLOBWB and W3RA. The idea behind this is to validate, verify and inter-compare real-time rainfall products as they become available. The first version of GLOFFIS went live in 2015, used internally at Deltares and by their customers. GLOFFIS is intended to be a research tool on predictability and interoperability first and foremost, but will be suitable for a wide range of applications once fully operational.

Further reading

Stephens E, Day JJ, Pappenberger F, Cloke H. Precipitation and floodiness. Geophys Res Lett 2015, 42(23): 10316–10323

Braman LS, van Aalst MK, Mason SJ, Suarez P, Ait-Chellouche Y, Tall A. Climate forecasts in disaster management: Red Cross flood operations in West Africa, 2008. Disasters 2013, 37:144–164

Thielen J, Bartholmes J, Ramos MH, de Roo A. The European Flood Alert System – part 1: concept and development. Hydrol Earth Syst Sci 2009, 13:125–140

Alfieri L, Burek P, Dutra E, Krzeminski B, Muraro D,Thielen J, Pappenberger F. GloFAS – global ensemble streamflow forecasting and flood early warning. Hydrol Earth Syst Sci 2013, 17:1161–1175

Posted in ensemble techniques, floods, operational systems | 4 Comments

Flood forecasting systems around the world – Interview with Thomas Adams and Tom Pagano

As the 1st Edition of the book “Flood Forecasting, A Global Perspective” has just been released, Thomas Adams (UCAR) and Tom Pagano (BoM) [see their bio below], editors, recall how the idea of the book was born and what they have learned when editing contributions describing national and regional flood forecasting systems from 11 different countries, as well as continental scale systems. This reference book points out challenges and ways forward to improve flood forecasting around the world.


Australian hydrologist David Wilson at the flood desk of the Victorian Regional Forecasting Centre. He is using the Bureau of Meteorology’s next generation Hydrological Forecasting System (HyFS) for operations during a large East Coast Low system in early June 2016 (© BoM)

Maria-Helena Ramos (MHR): What is the book about?

Thomas Adams: The book presents overviews of national and large regional operational flood forecasting systems as they exist right now around the globe and the operational challenges they face. Our intent is that the authors would provide some historical context that brought the systems to the point they are now. Tom Pagano and I felt it was critical that these systems should be described from the perspective of those that developed and use the system in their countries. We also included chapters on what appears to be the direction flood forecasting is advancing with continental and global forecast systems and the challenges they face moving forward.

MHR: Where does it cover?

Tom Pagano: Every continent is represented except Antarctica. We included developed and developing countries, from China to Colombia to the Congo. It also includes trans-national systems.

MHR: What was the original inspiration for the book? What were you trying to achieve?

Adams: The book began from something of a chance encounter with representatives from Elsevier wanting to offer to the hydrologic community a book covering topics in hydrology following the American Meteorological Society Annual meeting in 2014. I was on the agenda to moderate a technical session and was asked my thoughts for a book. I had thought for some time that something along the lines of the book was long overdue and suggested a book with this format, which Elsevier enthusiastically supported.

MHR: What did you learn in writing the book, what were your most surprising findings?

Adams: (Laughing) How relieved I would be with the completion of the book! With having seen many different approaches to the design and implementation of flood forecast systems, there are not many surprises. At the same time, it is gratifying that there are so many similarities between systems. This, again, is not very surprising since many of the problems are universal. The most troubling issue for me, which the book hopes to address, is the relative lack of understanding of National or regional scale systems and seemingly poor communication between those developing, implementing and operating these forecast systems.


Rapid growth in Chinese monitoring networks in the recent decade (chapter 3, page 72)

Pagano: Like Tom, it’s surprising how universal some problems are, such as data. Water is both a necessity and a hazard; for forecasters, the same is true for data. Data are our lifeblood and yet they can also be our downfall, such as when they are poor quality. I was also impressed with some of the real changes that are underway. For example, in just the past few years China has nearly tripled its already large raingauge and river station networks.

MHR: How are operational systems doing with respect to ensembles?

Adams: A few countries have ensemble products, including public products, but generally it has been an uneasy transition from deterministic to probabilistic products. I would liken it to the transition from point-based to graphical products in weather forecasting; initially there were difficulties and frustrations and questions about if this is what the user wanted and how we can keep experts in the loop… But now it is almost taken as given. There still is a major challenge with end-users accepting and understanding probabilistic forecasts — much needs to be done on this front.

MHR: Is there anything countries could be doing better?

Pagano: Given the common nature of our problems, there are so many opportunities for collaborations and exchanges. This might be easier in other fields – when one country produces an El Nino forecast, many countries can benefit and are interested. However, hydrology is very local and why would other countries care about the forecasts for a catchment in Australia? This leads to systems being developed in isolation, reinventing solutions. Here is where initiatives like HEPEX can come in, to give a sense of the big picture and to make connections throughout the international community forecasters, researchers and developers. Deltares has also built a community around its flood forecasting platform FEWS.

Adams: I agree fully with Tom. We need to be far more effective in opening communication between those that have developed National and large regional forecast systems. We do a very good job at communicating scientifically, but discussions of operational forecast system development and related issues are, too often, treated as secondary topics. An annual international conference dedicated to operational forecast systems aimed at sharing advances is needed.

MHR: The recent Hepex workshop held in Quebec made us wonder that although advanced methods and techniques exist (for ensemble prediction, data assimilation, post-processing, etc.), these are not always already implemented in operational systems. What advice would you give, for researchers and operational forecasters, to foster the science-operations dialogue in flood forecasting?

Adams: This is an excellent question. It goes without saying that there should be something of cooperative effort in the development and implementation of ensemble forecast systems in operational environments. What is missing is demonstration of clear and unambiguous evidence of the superiority of ensemble forecasts over single-valued, deterministic forecasts. This requires a paradigm shift in our thinking and will necessitate significant education and training of forecasters, but also the general public and decision makers.

Pagano: If ensembles are the answer, what was the question again? Do researchers and forecasters know who are going to use these new products, and for what? Operational agencies want to reap the benefit of their investments in their existing systems and are eager to innovate if there is a value proposition. This generally means fewer resources needed to make better products more reliably.

My other advice is to recognize that crises are important catalysts for change in institutions. We read several examples where major floods (or serious droughts) motivated stakeholders to see what more could be done to prevent the next disaster. This led to historic changes in arrangements, practices, technologies and so on. Clearly it helps to have good relationships established between scientists and practitioners before such events happen.

MHR: What is next? A Global perspective on operational drought forecasting systems? Any other projects in your minds?

Adams: I would love to plan for a 2nd edition, which would give current contributors the opportunity to update their chapters, but, more importantly, we would like to include additional contributors that were either missed or were unable to fit into their schedules the time to write a chapter. We also would like to strengthen sections on the future of flood forecasting and the problems facing the enterprise, specifically as it relates to human impacts and warnings. I have been approached by Elsevier to consider another project, which I suggested a similar volume dealing specifically with flash flood monitoring, warnings and prediction. I would encourage anyone who has interest to please contact me.

MHR: Any final thoughts to share with the Hepex community?

Pagano: I think there’s something for everybody in this volume, whether you are a researcher, forecaster, student or even just interested in history and institutions.

MHR: Thank you both for taking your time to answer these questions!

  • Where you can find the book: hard copy and electronic versions are available from Elsevier, Amazon and Google.
  • About the editors:

This is Thomas Adams

Thomas was recently a Visiting Scientist with the University Corporation for Atmospheric Research (UCAR) at the U.S. National Oceanic and Atmospheric Administration (NOAA), National Weather Service (NWS) National Water Center. His work focused on developing forecast visualization, analysis, and verification methods for the UCAR/NCAR WRF-Hydro based National Water Model and techniques to de-bias ensemble forcings and hydrologic forecasts. Previously, he spent a brief period with the Australian Bureau of Meteorology, where he befriended Tom Pagano (and the world changed forever), thus putting into motion a life-long collaboration! Currently, Tom is forming, a NGO to bring flood and flash flood forecasting to under-capitalized, developing and emerging countries: “A critical aim is to build technical expertise and a culture of understanding to sustain hydrometeorological monitoring, forecasting, and warning locally.Read more about Thomas Adams


This is Tom Pagano, getting his feet wet at the sandbag wall.

The unifying theme of Tom Pagano’s career has been using operational river forecasts to bring the benefits of science to the public. He leads a national flood modelling group within the Australian Bureau of Meteorology. He was also an operational river forecaster with the USDA Natural Resources Conservation Service in Portland, Oregon. He has designed, developed and helped implement forecasting systems on several continents. He also has a special interest in forecast verification and is the lead of a Bureau group for the communication of verification information. Read more about Tom Pagano


Posted in case-studies, interviews, operational systems | 1 Comment