Hydrological Web Services for Operational Flood Risk Monitoring and Forecasting at Local Scale in Niger
Abstract
:1. Introduction
2. Materials and Methods
2.1. Hydrological Web Services: Conceptual Framework
2.2. Study Area and the Sirba Flood Early Warning System Framework
2.3. System Architecture and SLAPIS Hydrological Web Services
2.4. Data and Data Model
2.4.1. Hydrometric Observations: Hydrometric Stations and Colored Staffs
2.4.2. Forecast Data: Hydraulic and Hydrological Models on Sirba River Basin
2.4.3. Flood Scenarios and Auxiliary Spatial Data
2.4.4. Data Model
2.5. System Implementation and Software Components
- PostgreSQL and PostGIS engine is the main component of the information management service layer. It is the system core for managing the geodatabase, geo-processing routines, and procedures.
- J2EE (Java 2 Enterprise Edition) and JAX-RS technologies are used for API (application programming interface) implementation, allowing the communication with other components and enhancing the interoperability with external clients and services.
- PHP/AJAX and OpenLayers technologies allow the web application to supply a custom graphical user interface (GUI) for data retrieval (observed and forecast) and map viewing.
- CKAN (Comprehensive Knowledge Archive Network) software [54] https://ckan.org/ (accessed on 22 September 2021) is used in order to implement the open source data catalogue following the OGC guidelines.
2.5.1. Information Management Services: Data Retrieval and Storage
- Bash scripts for contacting provider servers and downloading raw data;
- J2EE services for pre-processing and storing downloaded data;
- PL/pgSQL (Procedural Language/PostgreSQL Structured Query Language) procedures for forecast data optimization (Figure 4).
2.5.2. Processing Services: SLAPIS Hydrological Web Services
- External services that can be called by clients and other systems. These services are used to create a public information level on the hydrological scenario, flood scenario, and hydrometrics stations, allowing different stakeholders to monitor the evolution of hydrological season in real time.
- Internal services that are used for backend processing and are independent from a specific platform or implementation.
2.5.3. Application Services
2.5.4. Client: User Access Portal
3. Results
Operational Functionalities
4. Discussion
4.1. Interoperability of Hydrological Services
4.2. Dissemination
4.3. Challenges and Limits
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Hydrometric Station | Lat. [°] | Lon. [°] | River | Country | Basin (km2) | Installation Date |
---|---|---|---|---|---|---|
Garbey Korou | 13.73 | 1.6 | Sirba | Niger | 39.095 | 1956 |
Bossey Bangou | 13.35 | 1.29 | Sirba | Niger | 37.288 | 2018 |
Niamey 1 | 13.51 | 2.11 | Niger | Niger | 700.000 | 1929 |
Hydrometric Staff/Villages | Lat. [°] | Lon. [°] | River | Municipality | Installation Date |
---|---|---|---|---|---|
Touré | 13.61 | 1.44 | Sirba | Gotheye | 2019 |
Larba Birno | 13.70 | 1.55 | Sirba | Gotheye | 2019 |
Garbey Kourou | 13.73 | 1.60 | Sirba | Gotheye | 2019 |
Tallé | 13.76 | 1.63 | Sirba | Gotheye | 2019 |
Larba Toulombo | 13.69 | 1.55 | Sirba | Namaro | 2019 |
Web Services | Technologies | Format |
---|---|---|
Import data from hydrometric stations to DB | Bash, PHP | PostgreSQL data |
Discharge calculus | J2EE, JAX-RS | JSON, CSV |
Import GloFAS forecast data provider | Bash, J2EE, JAX-RS | PostgreSQL data |
GloFAS forecast data optimization | J2EE, JAX-RS, PL/pgSQL ? | JSON, CSV |
Import data from World-Wide Hype data provider | J2EE, JAX-RS | PostgreSQL data |
World-Wide Hype data optimization | J2EE, JAX-RS, PL/pgSQL ? | JSON, CSV |
Extract geographic structural layers | J2EE-JAX-RS /WMS | GeoJSON, SHP |
Extract flood scenario layers | J2EE-JAX-RS, PostGIS | GeoJSON, SHP, PNG |
Automatic report generator | PHP | |
Data output generator | PHP, J2EE, JAX-RS | JSON, CSV, PDF Excel, PNG |
Capabilities | Description |
---|---|
Visualization | Discharge and water level data/forecast/risk scenario |
GeoAnalysis/processing | Planned for the next version |
Reporting | Bulletins: automatic update |
Search and discovery | Data set, documentations, geographic layer |
Alert and notifications | Automatic internal alert; notification to national/local stakeholders |
Collaboration | Universities, research centers, national hydrological Institutions, international cooperation projects |
Content management | Metadata and documentation (in charge of system administrator) |
Resource management | Resource allocation (in charge of system administrator) |
Data management | In charge of system administrator or profiled users with API |
Decision support | Link to local planning documentation |
IT security | Conform to CNR and GDPR policy |
Other | Link to the reference project ANADIA 2 Blog |
Month | Dataset (Records) | Expected % | FileFTP (ΔT = 1 h) | Expected % | GloFAS (Records) | Expected % | WWH (Records) | Expected % |
---|---|---|---|---|---|---|---|---|
Jun | 720 | 100 | 677 | 94 | 30 | 100 | 16 | 53 |
Jul | 744 | 100 | 711 | 96 | 31 | 100 | 7 | 23 |
Aug | 743 | 100 | 638 | 86 | 31 | 100 | 25 | 81 |
Sept | 720 | 100 | 664 | 92 | 30 | 100 | 22 | 73 |
Month | Dataset (Records) | Expected % | FileFTP (ΔT = 1 h) | Expected % | GloFAS (Records) | Expected % | WWH (Records) | Expected % |
---|---|---|---|---|---|---|---|---|
Jun | 563 | 78 | 390 | 54 | 30 | 100 | 16 | 53 |
Jul | 744 | 100 | 577 | 78 | 31 | 100 | 7 | 23 |
Aug | 560 | 75 | 350 | 47 | 31 | 100 | 25 | 81 |
Sept | 544 | 76 | 305 | 42 | 30 | 100 | 22 | 73 |
Threshold Q (m3s−1) | GK Hours | GK Days | BB Hours | BB Days |
---|---|---|---|---|
600 | 1540 | 64 | 1565 | 65 |
800 | 1251 | 52 | 1302 | 54 |
1500 | 0 | 0 | 106 | 4 |
Type of Information | July | August | September | October | Total |
---|---|---|---|---|---|
1 day weather forecasts | 62 | 62 | 58 | 182 | |
3 days weather forecasts | 10 | 17 | 23 | 50 | |
SLAPIS bulletins | 8 | 9 | 5 | 22 | |
SLAPIS Posts | 11 | 16 | 9 | 36 | |
SLAPIS information notes | 4 | 7 | 2 | 1 | 14 |
DGRE National Alert | 3 | 3 | |||
Total | 95 | 113 | 97 | 1 | 307 |
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De Filippis, T.; Rocchi, L.; Massazza, G.; Pezzoli, A.; Rosso, M.; Housseini Ibrahim, M.; Tarchiani, V. Hydrological Web Services for Operational Flood Risk Monitoring and Forecasting at Local Scale in Niger. ISPRS Int. J. Geo-Inf. 2022, 11, 236. https://doi.org/10.3390/ijgi11040236
De Filippis T, Rocchi L, Massazza G, Pezzoli A, Rosso M, Housseini Ibrahim M, Tarchiani V. Hydrological Web Services for Operational Flood Risk Monitoring and Forecasting at Local Scale in Niger. ISPRS International Journal of Geo-Information. 2022; 11(4):236. https://doi.org/10.3390/ijgi11040236
Chicago/Turabian StyleDe Filippis, Tiziana, Leandro Rocchi, Giovanni Massazza, Alessandro Pezzoli, Maurizio Rosso, Mohamed Housseini Ibrahim, and Vieri Tarchiani. 2022. "Hydrological Web Services for Operational Flood Risk Monitoring and Forecasting at Local Scale in Niger" ISPRS International Journal of Geo-Information 11, no. 4: 236. https://doi.org/10.3390/ijgi11040236
APA StyleDe Filippis, T., Rocchi, L., Massazza, G., Pezzoli, A., Rosso, M., Housseini Ibrahim, M., & Tarchiani, V. (2022). Hydrological Web Services for Operational Flood Risk Monitoring and Forecasting at Local Scale in Niger. ISPRS International Journal of Geo-Information, 11(4), 236. https://doi.org/10.3390/ijgi11040236