Open for comments until 25 May 2021– Extended to 31 May 2021 – Leave your comment(s) below
A Logical Framework methodology was used to design a holistically consistent structure of goals, outcomes that leads to achievement of these goals, outputs that together will materialize into desirable outcomes, and finally activities through which outputs will be delivered.
A schematic illustration of resulting proposal is provided in the following figure.
The Table below provides a list of proposed activities grouped by outputs. More details can be found in the following table/document where for some of the activities additional features are proposed (such as responsibility, time, linkages, etc.). The Table is intended to become an integral part of the Action Plan as its annex.
ID |
activity |
Description |
E.1 |
Methods for standard assessment of data quality developed |
|
E.1.1 |
Guidelines on/development of practical methods for assessment (flagging) of hydrological data |
While metadata provides some information on data quality and reliability for particular use, additional assessment/classification of data uncertainty, or reliability (e.g. by flagging) might help research community in data processing. Providing guidelines and assess potential benefits of harmonized data assessment/classification system/tools |
E.1.2 |
Continuous development and update of Technical Regulation Vol. III and its annex on Hydrometry and other materials (including QMF-H compliance) |
|
E.2 |
Quality assured hydrometeorological data by NHSs are generated through increased compliance to the culture of Quality Management Framework – hydrology (QMF-H) |
|
E.2.1 |
Development of generic data production processes (schemes), metrics and internal guidelines (ISO 9001 like) |
Internal system of QMF comprising manuals, guidelines, defined processes and metrics is necessary for each enterprise, providing products and services |
E.2.2 |
Training materials and e-learning on QMF |
Review of requirements of Members on training in the field of QMF/QMS should lead to development of training plan for NHSs and its implementation based on identified priorities |
E.2.3 |
Information/promotion campaign – TED talks, what a difference quality makes |
What benefit investment in QMS brings, why it is key to deliver quality services? Let’s share good experience, and bad experience among community |
E.2.4 |
Field safety manual/training course |
Generic annotated structure of Field safety manual will help Members to develop their fit for purpose guidelines. Interactive courses, describing problems, sharing bad and good examples (like cyber security) is developed for practitioners in the field |
E.3 |
Improved (guidance for?) development and maintenance of technical platforms to support data exchange for research and science |
|
E.3.1 |
WHOS/WIS/WIGOS as a tool for data provision |
|
E.3.2 |
Evolving role of data centers helps Members in sharing and rescue its data |
|
E.4.1 |
Improved coordination on observing networks to fit the research purposes |
|
E.4.1 |
WMO, UNESCO-IHP, IAHS, ERB, FRIEND, data sharing network for hydrological science |
Based colloquium/conference on data for scientific purposes that would identify what and how to measure to enhance scientific concept paper for jointed distributed hydrological laboratory will be developed for further approval |
|
same as E.3.1 |
|
|
same as E.3.2 |
|
F.1 |
Enhanced culture of research & development to operation projects co-design (by operational hydrology and academia) – (Demonstration) projects are developed with beneficiaries being National Meteorological and Hydrological Services |
|
F.1.1 |
Catalogue of case studies/best practices (or may be bad practices as well) |
|
F.1.2 |
Database of research needs from NHSs as a project topics repository for scientist |
|
F.1.3 |
Implementation of research strategy for hydrology |
in cooperation with UNESCO and IAHS |
F.2 |
Inventory of the compiled data and products from Earth systems science projects for hydrological applications |
|
F.2.1 |
Similarly to activities on inventory of operational products, research outputs are compiled to be accessible for operational hydrology application where relevant |
See A.11, B.7 and C.2 |
F.3 |
Improved Earth system models outputs and its availability at high resolution for local and regional applications |
|
F.3.1 |
Improving QPE and QPF by research focus |
Global community of NWP and downscaling works together on providing QPE and QPF at relevant scales (< 1 square km) |
F.3.2 |
Guidance on coupled model systems, interfaces |
Provision of cases studies, Compendium of relevant methodologies |
F.4 |
There is a greater understanding of how the hydrological system responds to extreme conditions |
|
F.4.1 |
Tools and modules to assess and analyse uncertainty of extreme conditions are available. |
Research community further develops uncertainty and scenario analysis that can be directly used to design/manage infrastructure & water systems. |
F.5 |
Enhanced collaboration between hydrology and meteorology communities of practice, including academia |
|
F.5.1 |
Inclusion of different stakeholders (energy-water-food) needs and requirements, moving towards MHEWS approach |
Same as B.4.1 |
F.5.2 |
Widen the implementation of a Water segment to Regional Outlook Fora (ROFs) |
Same as C.7.1 |
Please comment on the list of activities by answering to the following questions using the “Leave a reply” form at the bottom of this page:
- Are any substantial activities missing from your point of view (please keep in mind that, given their nature, they may be also listed under cross-cutting issues)?
- Are all listed activities feasible and meaningful?
- Do you want to propose some changes to definition, description or other features of individual activities? Please refer to ID of the activity.
- Any other comments are welcome as well.
In addition please indicate, using the survey form here below, your preferences concerning the priorities by assigning ‘High priority’ to those activities you consider critical for the Action Plan for Hydrology and which you’d like to promote. At the same time, please assign ‘no priority’ to activities that should be downgraded or dropped from the Action Plan:
This post is open for comments until 25 May 2021 – Extended to 31 May 2021.
Thank you for your inputs!
José Zúñiga1 June 2021 at 9h06
On the goal: High quality data supports science.While the measure is ambitious and should be maintained, it is considered to be more about the volume than the quality of the data. One suggestion would be to define a percentage of the data collected that meets quality standards.
Johanna Korhonen28 May 2021 at 14h19
Increasing number of automated /continuous measuring instruments/systems needs both competence in devices, data transmission, data systems but also data quality assurance. Low cost and other ‘new’ technologies has to have also some guidelines for data management and quality assurance. The use of Artificial Intelligence should be increased in quality assurance. New methods develop very fast, technical regulations updating is slow process and they might be outdated when published.
For hydrological networks and data systems, maintaining sufficient resources in NHSs is important. It is easier to find money for developing new systems than keeping resources to maintain them afterwards.
Yashar Falamarzi26 May 2021 at 7h09
The view of WMO in supporting national and regional hydrological activities is highly appreciated.
nekooamal24 May 2021 at 9h21
Nekooamal from Meteorological Organization of Iran
Explanation of paragraph E.1.1:
In many countries, the meteorological and hydrological departments are separate, so meteorological organizations in these countries cannot evaluate hydrological data.
Explanation of paragraphF.1.1:
In developing countries and to large countries such as China and Russia, not all hydrological information is shared based on national interests, such as the quality of water resources.
Viacheslav Manukalo23 May 2021 at 10h23
In the process of developing new methods of observations and forecasts, scientists should pay great attention to the scientific support of their implementation in the operational work of hydrometeorological services. This is especially important nowadays, when there is a rapid development of advanced information technologies that are widely used in hydrometeorology.
Marcelo Uriburu Quirno13 May 2021 at 15h01
Referring to E.2: After adopting the culture of Quality Management Framework, it is also necessary to implement procedures for verifying that hydrometeorological data indeed have the intended quality.
It isn’t clear to me if this is already implied in E2.1 (metrics and internal guidelines). In case it is not, I suggest addressing this verification process.
Kristina Fröhlich11 May 2021 at 15h58
“F.4 Improved Earth system models at high resolution for local and regional applications
NHSs have the tools to assess and predict the current and future state of the water resources. Information is available to fully integrate surface and groundwater resources to improve earth system modelling and forecasting, in particular QPE and QPF.”
Comment:
I am unsure whether this statement is a stock check or a recommendation of equipment with all necessary tools. I think, that high resolution Earth-system models providing information for days to years are not yet state-of-the-art, so that hydrologists need to find ways around this.
Beside an improved and high-resolution Earth system model, statistical methods for downscaling or machine learning approaches offer a qualitatively good, fast and computationally cheap method to obtain higher resolution forecasts, especially on the long term. Is a list of recommended tools available in the guidance document and will this guidance be updated on a regular basis to allow for new developments?