Climate change is posing unprecedented challenges to water authorities all over the world. Rapidly rising sea levels, more severe rainstorms and prolonged droughts are the results of significant changes in atmospheric behaviour, as well as shifts in the hydrology of river basins. Water resources managers therefore have to rethink their plans, prepare and take urgent action to ensure new forms of protection which are designed, built and managed along rivers and coasts to safeguard areas of high economic activity and where the majority of the world’s population lives.
However, protection alone is not enough; in the near future, the increased elevation heights of pumping stations mean that discharging excess water from low-lying deltaic areas will also demand far greater efforts. Water availability in terms of retention and discharge is of key importance in drought-prone areas, as well as for protecting lower reaches of rivers against salt intrusion from rising sea levels. This is a huge task: the UN has estimated related worldwide investment costs of USD 750 billion a year for decades to come.
The traditional designs of water systems and water infrastructure are based on long-term statistical records of hydro-meteorological variables. Owing, however, to systemic changes in the atmosphere and in river basins, these records are no longer representative of current and future situations and are thus of increasingly little value. This fact is equally important for the operating and maintenance of water infrastructure.
Dealing with climate change is becoming extremely challenging, while the increasing scarcity of fresh water means it is essential to use every drop of water as effectively as possible. In South Africa, for example, domestic water is in increasingly short supply, and there is even a risk of no tap water at all being available (on what is known as ‘day-zero’). In Australia, too, a country where every type of climate zone can be found, all forms of weather-related extremes are now occurring more frequently, with 2022 seeing increased numbers of bush fires, as well as excessive drought and extreme and lasting precipitation accompanied by large-scale flooding. That year also saw extreme flooding in Pakistan, where one third of the country was under water for weeks, causing millions of people to flee and thousands of deaths. While the effects are not yet so clearly visible in some other regions of the world, sea levels along the Dutch coast are predicted to rise by 2 to 3 metres by the end of 2100 if current greenhouse gas emissions continue. That will pose a serious challenge in a country with subsiding soils and the majority of its surface below mean sea level.
Water managers can help alleviate many of these problems by enhanced management of water throughout the year. This could include water retention systems, groundwater recharge, controlled discharges, and balanced reservoir management and irrigation. The focus of water managers’ tasks is increasingly shifting, therefore, towards helping society to become more weather- and climate-resilient.
This white paper targets an audience of professionals involved in integrated water resources management (IWRM) and shows how digital services can help water managers to perform better in times of climate change. We focus on decision support systems (DSS), showcasing HydroNET and its implementations, and explain how such systems are used in practice and how they benefit the water sector and all its stakeholders.
2. The way forward
Although we do not yet know the full implications of the likely massive changes ahead, we know that the way we have protected our environment in previous centuries is certainly not going to be sufficient anymore. The next question that then arises is which approach to follow when redesigning and operating water resources systems. Current policies prescribe adaptive approaches focusing on directing change, rather than on absolute values, and with scope for updating when new information becomes available.
“Optimizing the operation of our water systems is a very effective way to deal with scarce resources.”
Adaptive approaches are particularly difficult for infrastructural measures, whereas water systems’ operations are much more flexible. Optimizing the operation of our water systems by using the full capacity that water systems and their infrastructure offer, is therefore a very effective way to deal with scarce resources. This can include optimizing reservoir management in irrigation schemes or optimizing discharges of water from drought-sensitive areas. Past measures have often focused on one end of the spectrum: drought or flooding. These days, however, we have seen more water systems experiencing both of these problems and at varying times of the year. We therefore need to become more resilient and to adapt our water management practices to prevent unwanted system behaviour throughout the year.
While optimizing our use of available water resources alone will not be enough to overcome the climate challenge, it is a means to postpone expensive investments in new infrastructure and in the meantime enable us to acquire more information about the extent of climate change and its real effects on our environment. Optimizing the operating of water systems will result in lower investments because the costs of the monitoring and control systems are of a lower order of magnitude than investments in infrastructure.
3. Optimizing the operating of water-resources systems
The operating of water-resources systems is generally based on the requirements of the various stakeholders: urban, agriculture, industry, energy, shipping, nature, etc. All these requirements have to be met as effectively as possible through water infrastructure such as reservoirs, sluices, pumping stations, inlets, etc. Operations are regarded as optimized when all these stakeholders’ requirements are met. With limited availability of water, however, as well as water systems‘ limited capacities, the general optimum cannot always be reached.
Many methodologies exist to ensure that the entire water system is controlled optimally or near to the optimum. All these methodologies, varying from heuristics to computer-based approaches, are applied in practice. They can include, for example, an operator manually controlling a pumping station on the basis of local water levels monitored (heuristics), or a fully automated system controlled by optimization algorithms fed with monitoring data and forecasting data (computer-based).
All these operational approaches use monitoring data to determine the current state of a water system. These days, we have a wealth of data sources available through advanced sensor networks such as hydro-meteorological monitoring networks and satellite-based remote sensing. Up-to-date in-situ data from across the world is available for water management, and significant data sets – on, for example, rainfall, evaporation and the amount of water in the soil – are available from satellite imagery from even the most remote areas. Such data is extremely valuable for managing water resources and allows models to be used to determine unmeasured values of water system variables (also called ‘soft sensors’).
“Data from satellite imagery is extremely valuable for managing water resources”
Apart from monitoring data, meteorological, hydrological and hydrodynamic models help to determine near-future states of water systems, particularly in combination with recent historical and current monitoring data. By updating models and assimilating their parameters to accommodate the latest monitoring data, water authorities can improve their forecasting of excessive situations and predict potentially hazardous circumstances.
Two types of models are used to support forecasting:
- Physically-based models, which have existed for a long time, contain descriptions of natural laws of movement, such as vapour in the atmosphere and water flowing in the physical environment. Discretization of continuous physical processes in equations is also applied, as well as numerical schemes to solve the equations step by step. But with highly non-linear processes, this may become time-consuming.
- Data-driven models, which are based on data rather than on physics, learn from available data series and are able to reproduce non-linear behaviour in the atmosphere, as well as in water movements in the sub-soil and surface waters. After a computationally-demanding training of these models, they can usually compute very rapidly, and this makes them very useful in operational situations such as real-time control of water systems. Data-driven models are very adaptive to new data and relatively easy to retrain to reproduce new water-system behaviour. The changing climate and changing water-system behaviour mean we can expect them to become mainstream.
4. Decision support systems for water management
The entire water management process – from water-system monitoring and data processing to operations – is facilitated by computer-based systems known as ‘decision support systems’. These systems contain all the elements described in the previous section (meteorology, hydrology, modelling, forecasting and operations) and help water managers to take timely, well-informed and optimal decisions.
We distinguish three types of decisions in water resources management:
- Operational: focusing on the short term, e.g. which action needs to be taken now, in real time; usually this involves a state-of-the-water-system forecast covering a period of several hours up to a few days;
- Tactical: focusing on a longer time scale, which may set the limits of operational control (e.g. water availability management during an entire season) to be achieved by controlling reservoirs;
- Strategic: focusing on a long-term weighing of interests, e.g. to determine the capacities of water infrastructure such as canals, pumping stations and reservoirs.
The increased demands being placed on our water systems, as well as the changing climate and growing world population, are resulting in increasingly complex water-system control situations and mean we will increasingly depend on the outcomes of decision support systems.
Decision support systems generally consist of the following layers:
- Dissemination: visualization, interaction, dissemination and reporting
- Domain intelligence: data analytics and modelling
- Data integration: processing, storage and exchange
- Monitoring: collecting and storing data.
Decision support systems in the water management domain (including present-day information technologies) consist of the following components:
- Monitoring systems for data collection and storage of historical and real-time data from water monitoring networks: in-situ measurements, telemetry data, data from supervisory-control-and-data-acquisition systems (SCADA) on e.g. water infrastructure operations, remote sensing data of e.g. radars and satellites, and weather-services forecasting model outputs;
- Data integration and storage of multi-dimensional historic, current and forecasted data sets – geographical, infrastructural and monitoring data – for further analysis and exchange;
- Water intelligence through automatic and user-requested data analytics and simulations based on historical, current and forecasted monitoring data, water knowledge and operational modelling of weather and water conditions;
- Dissemination of thematic information through digital control rooms with customized information on weather, water and climate with advisories and alerts.
5. HydroNET DSS implementation
The HydroNET decision support platform has been implemented in line with the water management DSS described above. It is designed to help professionals working in water-resources management to take balanced, timely and well-informed and accountable decisions and to take actionable measures at operational, tactical and strategic levels.
Many different professionals – currently more than 8000 across the world – working for water authorities use the system in their day-to-day work. HydroNET was originally developed to help Dutch water managers with their complex task of managing water in river basins and canals and in pumped systems such as polders. Over the years, HydroNET has evolved into a generic system for managing drainage, water supply and water quality in areas with various competing interests. It is currently used for operational control by 95% of Dutch water boards and by the national water management authority (Rijkswaterstaat).
“More than 8000 professionals across the world are using HydroNET for their day-to-day work.”
The following are just a few examples of how HydroNET applications are used in different parts of the world:
- In Australia, water authorities use HydroNET for water availability monitoring and management (water auditing), as well as for flood risk management;
- In South Africa, all the regional water authorities use it for managing the distribution of water in large catchments to ensure good-quality water is available for energy production, irrigation and domestic use, as well as for managing excess water and flood risk;
- In Germany, local and regional water authorities use HydroNET for urban drainage purposes and specifically to monitor, analyze and forecast local excessive precipitation in real time;
- In the Netherlands, it is used for managing salinity intrusion in the estuaries of the country’s major rivers and for managing water levels in polders and storage basins. Three water boards use it in combination with high-water emergency response software for dike monitoring, operational dike reinforcement with sand bags, calamity management and evacuation warnings.
These are just a few examples of how HydroNET is currently being used for water management purposes in various countries. One of the features of this DSS is its versatility, allowing it to be configured to support water professionals in virtually any water resources management task.
HydroNET can be used by operators in the field performing local tasks, by water managers in control rooms and by water authorities’ policymakers up to management board level. HydroNET is fully customizable to enable this wide range of applications and meet the needs of water authorities. Where tasks cannot be performed by HydroNET, the system can seamlessly connect to services provided by water authorities themselves and to third-party services.
HydroNET can also provide services to other systems, thus enabling its unique features to be used in other information systems. In general it can be seen as a digital system for integrating and exchanging data, information and knowledge in the water domain.
HydroNET has the typical DSS structure described above and shown in Fig. 1, with components of monitoring, data processing, water intelligence and user interfaces. One of the choices made is to provide only generic DSS services. These can then be configured to meet users’ needs using just HydroNET. HydroNET services can also be made available at systems of water authorities or systems of other providers.
“The policy is not to duplicate services already performed by other professional providers.”
The policy is not to duplicate services already performed by other providers at advanced levels. In the following sections we describe the main internal and external functionalities of the HydroNET DSS, ranging from monitoring to the user interface.
5.1 Monitoring systems
By using available functionalities of external monitoring systems and databases, HydroNET is able to seamlessly integrate all the necessary data. In other words, it does not replicate generically available functionalities. The available data sources may have equidistant or non-equidistant (time series) intervals and various spatial properties, such as point data and spatial data up to 5D data sets. This means one virtual distributed database is created from a wealth of data sources:
- Sensor and IoT networks of water authorities providing, for example, local data on infrastructure operations from SCADA systems or telemetric systems;
- Data from national weather services providing weather monitoring data such as data measured with rain gauges or radar-monitored precipitation;
- Data from space agencies such as NASA and ESA, which provide satellite-based earth-observation data;
- Data from providers of specialized hydro-meteorological data, such as actual evaporation data, amounts of water in the soil, etc.
HydroNET also connects to the external models running automatically, e.g.:
- Weather-model outputs of, for example, NOAA and ECMWF, which provide 10 and 15 days forecasts respectively of weather variables across the world, every 3 hours;
- Hydrological and hydrodynamic models running in third-party environments, such as Delft-FEWS and AquaSafe.
All data connections can operate in real time, which ensures information is up-to-date. HydroNET contains many prepared and ready-to-use connectors, and this array of connectors can easily be expanded to include new ones. If live connections with external data are not possible, HydroNET can download the data and make it available locally. In essence, therefore, two data flows exist: ‘connectors’ (i.e. live data) and ‘collectors’ (i.e. downloaded data). Collecting the data can be useful also to enhance performance in case of extremely large gridded datasets.
Some data sources are provided free of rights, while others are licensed. HydroNET has various mechanisms in place to ensure that intellectual property rights are respected and that users can only access data sources to which they have been authorized.
5.2 Data integration
When connected to the data sources, HydroNET services register the meta data (such as the data owner, data location, grid projection and measuring interval) and create a catalogue of monitoring variables for further internal and external use. By doing so HydroNET adheres to the FAIR principles (Finable, Accessible, Interoperable, Reusable).
Internal and external ‘processors’ can also be applied in a flow of computations for processes such as correction, calibration, merger, upscaling or downscaling, geo-spatial pyramiding, reformatting for rapid access by other services, and archiving. Internal HydroNET functions exist for all these processes and, if required, user-defined functions can also be run within the system by means of scripting; a Python parser is provided. Lastly, access permissions can be set for each data source separately.
Some processes require data to be integrated, e.g. sequential correcting of rainfall radar data with ground monitoring when new radar images or rain gauge data become available. Data may also need to be merged with other external data sources, such as geo-spatial overlaying with boundaries of hydrological units or catchments, or water authorities’ administrative boundaries. This also requires connections to external GIS (geographical information system) and BIM (building information modelling) data provided by water authorities and other government organizations.
All processed data is available for internal HydroNET services, as well as for external data users, via a standardized API (application programming interface) that respects user access credentials.
5.3 Water intelligence
All area- and user organization-specific information and knowledge are embedded in this component. In the operational setting this may vary from simple operational rules to compute the exceeding of threshold values (such as when monitored water levels exceed maximum levels) to complex simulations (such as forecasting the flooding of areas a few days ahead). All computations can be performed using the available data, which consists of current and forecasting data, as well as historical data gathered over many years. The configuration depends entirely on the needs of the water authority and its water managers.
There are two ways in which computations can be configured in HydroNET:
- Computations performed by internal modules; for instance, processing of knowledge rules with ‘if-then-else’ logic, where all internal variables can be used, as well as fixed parameters;
- By external logic in Python or other programming languages, or by external modelling shells such as Delft-FEWS running HEC-RAS, Sobek, D-Hydro, Mike, SWMM or other hydrodynamic software.
In the latter case, three options for coupling exist, all of which use the generic HydroNET API to access water-system data:
- Offline coupling and further processing by external software, in which case the computed data is also accessed by external software and HydroNET is used only as a data warehouse.
- Step-wise coupling with external computation using HydroNET data and a HydroNET connector to the computed model output. This is applied if the external computation has its own scheduling, e.g. external physically-based forecasting models in FEWS.
- Online coupling with direct feedback of computed results into HydroNET, and using its API. This approach allows interaction on request between HydroNET and the external application, and is most appropriate for running validated scripts and data-driven models.
Computational outputs can be further integrated with HydroNET data and logic to perform analyses of historical, current and forecasted situations. If the computations show alarming levels being reached or surpassed, various mechanisms can be activated to warn DSS users. Alerts may be issued by HydroNET and made available via user interfaces on, for example, traffic light maps. Alternatively they can be issued by the DSS services for use by external systems or via reports and sent directly to end users by e-mail or instant messaging systems.
The graphical user interface of HydroNET and its functionalities can be used in various ways, including on video walls, desktops, laptops, tablets and mobile phones. All users can access all DSS functionalities at the same time. The entire interface is fully user-configurable as interactive maps, charts and user-specific tools are all integrated into tailored dashboards. All configuring can be performed by users themselves, using the HydroNET toolbox, e.g. Dashboard Manager, MapTool and ChartTool.
Users can define as many dashboards as they desire; together, the dashboards are referred to as a ‘water control room’. Dashboards and their contents can be kept private for use by a single user, or be shared with users or groups within their organization or even with other organizations using HydroNET. Some parts of dashboards can be shared via embeds on external websites.
The DSS can be used as an intelligent viewer in addition to all the data and information, or it can be used as a digital twin to drill down and obtain multiple views of the data, information and modelling outputs. User-specific visuals delivered with third-party software such as Microsoft PowerBI can be integrated into HydroNET’s dashboarding approach to create a dynamic blend of infographics.
Like the data-processing and the water-intelligence components, parts of the user interface component can also be made available externally. Such external use of HydroNET functionality can be implemented if water authorities already have IT systems in place and want to integrate HydroNET functionality into these existing systems. Dutch water authorities have often used ESRI ArcGIS StoryMaps to present DSS results in a story-telling manner. This approach is basically possible with any third-party visualization system and also with any external digital twin software.
5.5 User community
HydroNET functionality has been developed in co-creation with professionals from water authorities and with the involvement of meteorology, climatology and water management scientists, as well as in close cooperation with water consultants and IT professionals.
“HydroNET functionality has been developed in co-creation with professionals from water authorities.”
From the early stages of the first version of the product in 2004, end users have been involved in the design and development process. As a result, many of the required IWRM functionalities have found their way into the DSS. Significant requests implemented as a result of end users’ involvement include:
- Seamless integration with existing data processing and modelling systems;
- Ability to connect to any monitoring source, while respecting data rights;
- Ease of use for different types of end users working in the field, both in offices and control rooms;
- Personal interfaces containing data, information and knowledge, as well as generic interfaces for the water authority as a whole;
- Integration of end user-specific functionalities: computations, analysis and visualizations;
- A single approach for sharing information with colleagues inside and/or outside the organization;
- A single system for all users, running via the web and without any installation worries at the user end;
- Cost and profit model where new functionalities become available to all users in the community and operating costs are shared.
HydroNET’s user interface facilitates cooperation between various parties in the water domain, with many examples of water authorities sharing data through HydroNET and teaming up to jointly manage water systems, often on a transboundary basis.
A strong HydroNET user community has developed over the years. Within this community, users share experiences with the DSS and together come up with new ideas for applications. Members of this international community meet in person once every two years at HydroNET Live events. End users from water authorities, HydroNET service professionals, IT designers and architects, and water industry consultants gather together at these events to exchange details of their experience with the DSS.
6. Information technologies used
Over the years developers and architects have learned from the use of HydroNET DSS and have updated the information technologies to ensure a future-proof software stack. While the previous HydroNET generation focused on specific applications for specific decision support uses, the current fourth generation of HydroNET is modular and focuses on configuring the system to suit users’ wishes. Configurations can be made across the entire DSS sequence, ranging from monitoring, data processing and water intelligence and up to the user interface.
The IT design now also includes the use of microservices, a service bus, cloud scaling, and web-based and responsive user interfaces. The operational software is deployed using automated pipelines to cloud instances in different regions of the world.
6.1 Software and hardware environments
Since 2004, HydroNET has evolved from an initial desktop version, via a client/server version, to a fully cloud-based stack. The current version has been further developed in line with a functional and non-functional requirements roadmap and is maintained by a team of 10 professional software engineers and architects. At present, the software base is managed using DevOps approaches, with automatic release pipelines using CI/CD (continuous integration and continuous delivery) for testing, development and production stacks.
The code has been developed using C#.NET Core for the back-end and middleware processes and Angular/TypeScript for the front-end. It also uses an array of libraries with standard components for user interfacing, authentication and authorization, as well as for spatial arithmetic, shared building blocks and more.
The software runs in a cloud environment, currently in different Azure regions, which basically enables scaling of services. Scaling, which is currently implemented, allows many users to use the DSS in parallel, such as when an extreme event is ongoing and emergency management by water authorities is needed. In these situations, an avalanche of data requests may load the system to its limits. Upscaling, using more hardware resources, is then required. After such an event, downscaling of resources ensures effective cost management, which is essentially only possible in a cloud environment.
To facilitate up- and downscaling, all HydroNET components have been developed as microservices, communicating through an internal service bus that provides information from databases and among microservices. Everything runs in Docker containers, orchestrated by Kubernetes clusters.
One of the important tasks of a DSS is to be well-connected to external data, information and modelling services. Substantial efforts have therefore been undertaken over the years to ensure seamless integration of these external sources into HydroNET, and vice versa. There are currently more than 100 database connections (and their number is increasing), using appropriate data formats and standards. The generic standards supported include WaterML2, SOS2, Digital Delta Aquo, FEWS-PI, GEOSS, Grid, TIFF, OGC WMF, WSF and many others. Very specific formats and data projections used in meteorology and climatology are also supported, including the ECMWF-EPS and GEFS global weather probability forecasts.
The relevant standards and formats are supported for exchanging information with external systems across the entire DSS stack, from monitoring to the user interface.
6.3 Data connectors
HydroNET can be connected to a wealth of external monitoring data sources. All these connectors contain mapping of the external data formats to the internal HydroNET variables, including versioning. Efficient connectors connect to external APIs, with clear descriptions of, for example, data sources, units and meta data on the most recent measurements. More traditional connections also exist, using downloading from external sites (via secure transfer protocols) for data access.
There are essentially two types of connectors:
- Connectors that create live links with the original remote data;
- Connectors that download the data (‘data collectors’).
The preference is for live connectors because they ensure access to the latest versions of the source data, even when the data has been updated by the data owner. From an intellectual property perspective, too, live connections are preferred because they guarantee that the original data resides on the owner’s systems and is accessed only when required.
To ensure fast connections, some connectors support caching technologies that capture a snapshot of the latest data retrieved; the associated data redundancy on the HydroNET side of the connector then enables data access even when the data source is temporarily unavailable.
Industry-standard databases structure the monitoring meta data, as well as the user data, with generic organization and specific user settings. Various approaches are followed for storing the monitoring data itself. The primary method involves using the multi-dimensional storage formats used in meteorologic and climate sciences, such as HDF, GRIB and NetCDF. These are very efficient binary formats that permit fast access to spatial subsets of the data. Spatial data usually has three dimensions: time, longitude and latitude. In weather and water modelling, however, other dimensions also exist – such as model run time and ensemble run – in the event of probabilistic computation. This means data storage in five dimensions is required for the majority of the spatial data in the DSS.
HydroNET follows a security-by-design approach for software and hardware. Security awareness among the development team is maintained by applying SDL (Security Development Lifecycle) during all stages of software development, namely:
- Security architecture check in the design phase;
- Secure coding reviews during the software development;
- Intrusion scanning to detect vulnerabilities in the software and infrastructure;
- Frequent patching of external software components and hardware operating systems to minimize vulnerabilities.
HydroNET incorporates various levels of security to prevent unauthorized access to the system and its data:
- Firewalls between internet and the DSS hardware;
- Application of authentication (who) and authorization (permissions) for all users, including administrators;
- Authentication and authorization implemented in all microservices to ensure permitted access across the entire software and data stack;
- Use of VPN (virtual private network) tunnels, IP-whitelisting and digital certificates for external access;
- Use of access tokens to keep end-to-end connections secure.
HydroNET applies a logging practice of continuously gathering, storing, processing and analyzing data from services and hardware in order to optimize system performance, identify technical issues, manage resources, strengthen security and improve compliance. All logging is stored offsite to ensure access even when the system is down. In addition, specialized external dashboarding services contain automatic analysis, drill down, warning and reporting functionalities, which permits pro-active management and end-user support.
7. HydroNET business case
Use of a DSS is vital for effective water resources management, both now and in the future. These systems generally represent a very efficient way to make optimal use of all available resources. The benefits of a DSS such as HydroNET include:
- Damage reduction during and after natural hazards, including lives saved;
- Cost savings on infrastructure and monitoring expenditure;
- Time savings for staff involved in water-system operations.
7.1 Reducing damage
A DSS helps water authorities to anticipate situations where the risks of natural hazards are high and may result in flood damage, drought damage, water quality damage and loss of life. A DSS helps to identify in advance the extent of extreme weather and its long-term consequences for all stakeholders in the water system, taking account both of climate-change effects and day-to-day operational situations.
Based on predictions, water managers may decide to take prompt action to prevent damage or to reduce the extent of inevitable damage. In the operational case, the final advisory given may be to evacuate an entire region if flooding risks become too high.
The potential for damage reduction in the event of floods and droughts is enormous. Damage caused by natural hazards alone around the world costs as much as USD 100 billion each year. Even if only a fraction of this damage could have been avoided, investing in a DSS would be worthwhile for any water authority.
7.2 Saving costs
Once connections with data sources have been established, there is no need to make another connection with that data, e.g. for another application. HydroNET’s data warehouse ensures that all data is easily accessible and that new uses of the data, also outside HydroNET, do not require new connections to be made. Simply connect to the generic HydroNET API and all data is available in a standardized form. This approach can save 70-80% of software development and maintenance costs (i.e. the amount of work usually associated with making IWRM data operational for an application), compared to more traditional approaches.
"Reusing HydoNET’s API can save 70-80% of software development and maintenance costs."
Different sources containing similar data may be available, such as rainfall information from gauged networks, rainfall information from radars and rainfall information abstracted from satellite data. Depending on the application, water managers can choose the most appropriate data source. Experience with the use of radar rainfall information shows this information to be extremely efficient once it is operational and has been adjusted to include rain gauges from the field. Radar is well suited to capture the spatial variability and detail of rainfall, compared to rain gauges. In many cases water authorities that have access to this data can limit their expenditure on installing and subsequently operating rain gauges.
Informed water-system operations also reduce infrastructure investment costs. By making better use of storage basins and operating infrastructure in a more balanced and anticipatory manner, Dutch waterboards have been able to save or postpone millions of euros of investments in expanding pumping station capacities, without increasing the risk of flooding.
Lastly, water authorities can save substantial amounts of money by using a cloud-based and sharing DSS such as HydroNET. Predictable subscription costs are all that is needed, with no need for own investments in ICT, maintenance or software licences because all these costs are shared with the other users of the system.
7.3 Saving time
The argument that automation saves time is often used to justify introducing new software. But rather than simply saving time, in practice we often see that automation enables professionals to switch from routine work to work based on more in-depth knowledge, focusing on more detailed analytics and quality improvement. Here, therefore, we interpret saving time as meaning reducing the time and effort spent on routine work.
"In practice automation enables professionals to switch from routine work to work based on more in-depth knowledge, focusing on more detailed analytics and quality improvement."
There is one activity of water authorities, however, where using HydroNET saves a lot of time, and that is specifically the time spent storing, managing and backing up climate and weather data from weather services and space agencies. These time-consuming activities are all outsourced to HydroNET, which fully automates these processes. Water authorities obviously have to manage local monitoring data in telemetry and SCADA systems, but this should be seen as a key task of water managers who know the value of this data and are able to use local knowledge to validate it.
Data monitored by several sources and pre-processed by HydroNET, can be merged into a single source to create the best of all worlds. An example is rainfall forecasting comprising global models (up to 2 weeks), regional models (up to 2 days) and radar nowcasting (up to 2 hours). Every 5 minutes, the DSS can seamlessly create a real-time time series of 14 days that contains all the information in a systematic, transparent and reproduceable manner, ready for implementation in a flood forecasting system, for example. This considerably reduces the day-to-day work that, without such a system, would have to be done manually and with a much lower time resolution.
Many HydroNET uses result in automatic reports being generated. Two key examples are:
- The flood reports generated for insurance purposes after a period of flooding in New South Wales, Australia, when more than 10,000 reports per week were generated;
- The weekly analytical drought reports generated by waterboards in the Netherlands during dry summer periods.
The level of detail provided in these reports would not have been possible without all the data sources at hand and automated processing.
7.4 DSS business value
Importantly, web-based DSS like HydroNET have a highly predictable cost model. The costs incurred by water authorities are the costs of the system’s one-off implementation; annual subscription costs for support, updates and upgrades; and end users’ costs of operating the system. For most water authorities, the time savings achieved by using HydroNET cover the full annual costs of its operation, with an order of magnitude of 1 fte (full-time equivalent). Of course, the exact costs and savings will depend on the intensity of use and the size of the organization.
This means, then, that all the other benefits come more or less without cost. This, in turn, means a very profitable TOC (total cost of ownership), particularly if the costs of infrastructure investments can be circumvented or deferred, and if informed operational decisions result in reduced damage from, for example, flood events, periods of drought and salinity, as well as in fewer casualties.
8. HydroNET DSS in action
So how does HydroNET differ from other solutions available in the water market? First of all, hardly any other DSS products focus on IWRM. The majority of products have been developed for water utilities and have been expanded to include functionalities for other domains, including water management. Rather, however, than using this paper to present a market analysis, we have chosen to focus on the proven success factors of HydroNET and in particular to focus on how end users have co-determined the product’s shape and on those functionalities where HydroNET is unique.
Practical experience of the HydroNET DSS and how it is used by a wide range of end users at municipalities, waterboards, central governments, provinces, industry partners, consultants and providers of complementary software products has shown that it comprises an array of unique features, as described briefly below.
- A wealth of water, weather and climate data sources, as well as operational, telemetric and SCADA data, can be seamlessly integrated into the DSS, with live connections to the original data sources and without data duplication;
- Optimized structure for spatio-temporal data sets (up to 5D data);
- A data archive is built up over time, and third-party archives can be imported into this;
- Interoperable trough straightforward connection to other platforms and modelling systems;
- Programmable data flows to automatically import and merge external data with internal data.
- Optimized for IWRM needs, with embedded water-domain knowledge from users, water professionals and water and climate scientists;
- Support for water managers who have to balance the interests, for example, of agriculture, nature, shipping, industry, energy production and water management;
- Integration of various simulation and added-value services: knowledge rules, Python scripts, digital twins and external hydrological and hydrodynamic models;
- Toolbox for hydrological data and information analysis that can be used interactively;
- Actionable advisories to optimally use every drop of water for all stakeholders’ benefit.
- Interactive services available to other systems: embedding expert knowledge, digital twins and water simulation outputs.
- Digital water control room, integrating portal environment for use by different stakeholders on video walls, desktops, laptops, tablets and mobile phones;
- Straightforward configuring of user-specific, situational and contextual dashboards, reports and alerts in support of timely actions, including mapping and graphing functionalities;
- Historical, live and forecasted information accessible through user-friendly, flexible and interactive interfaces.
- All visualization can be shared with other users or be exported to other third-party systems, including live feeds to web-enabled applications and websites;
- Strong cooperation tools for truly integrated, transparent and transboundary water management, where water authorities join forces to optimize water control so as to minimize damage and costs;
- Developed with and for water management professionals through enhanced knowledge and experience-sharing during webinars and community events;
- Software developed in co-creation with the water sector; sharing of operating and maintenance costs.
Operations and IT benefits
- Proven web platform with more than 8000 end users who rank the platform and its support at 8.5 on a scale of 1 to 10;
- Single point of access for all relevant historical, real-time and forecasted data;
- Data warehouse solution with advanced API;
- High level of security through advanced authentication and authorization and secure data connections;
- No IT hassle for users and their organizations: all software runs on the internet and is visualized in a web browser;
- Cloud-based and scalable software and hardware solutions to ensure high performance;
- Frequent and seamless software updates and upgrades for all users.
9. Overcoming IWRM challenges
Current weather and climate extremes are presenting new challenges for water managers taking strategic, tactical and operational decisions while also having to meet all the requirements of water-system stakeholders. Design and operation practices used in the past no longer apply, are inefficient in the current circumstances or may soon no longer work. A DSS is the tooling inevitably chosen in complex IWRM situations requiring well-informed decisions on the design and operating of water infrastructure.
A DSS such as HydroNET helps to make the decision-making process more transparent and efficient in the following ways:
- Decisions can be based on data and information, together with knowledge of water-system behaviour, rather than on past experience;
- New generations of water managers can be trained using the DSS to replay previous situations and learn from experience and mistakes;
- When new weather and climate data becomes available, the system can adapt its internal algorithms and present up-to-date advisories;
- More efficient use of all resources helps reduce operational costs, minimize damage and limit infrastructural investments to the absolutely necessary levels;
- Managers of water resources systems become better informed, gain more knowledge on water-system performance, can develop more actionable strategies, and concentrate more on analyzing and learning from continuously changing data.
Using a DSS obviously does not prevent natural hazards from occurring. However, using such a system is the best way to ensure managers are better informed and can take balanced decisions that take as much account as possible of all stakeholders’ interests, while minimizing costs and damage and avoiding casualties.