Strategic Studies of public policy

Strategic Studies of public policy

Compilation of Strategies for Developing National Weather Data Infrastructure to Enhance Water Governance in Iran

Document Type : Research Paper

Authors
Mahsa Ghotbizadeh 1
Mahdi Zarghami 2
Ali Abdollahi-Nasab 3
Arash Taghipour Zarei 4
Mahdi Nazari 5
1 PhD student,Tabriz university, Tabriz, Iran.
2 Professor, Faculty of Governance, University of Tehran, Tehran, Iran.
3 Assistant Professor, Technology Studies Institute, Tehran, Iran
4 Researcher, Technology Studies Institute, Tehran, Iran.
5 Member of Water Group of Center for Progress and Development of Iran,Tehran, Iran.
10.22034/sspp.2024.2026850.3613
Abstract
The development of water and meteorological data infrastructure ensures the quality, security, and integrity of water and climate data, thereby enhancing policy-making in the water resources sector, environmental decision-making, and meteorological forecasting. Despite the significance of this issue, the collection, transmission, processing, storage, production, and dissemination of climate data and information in Iran faces numerous structural and institutional challenges. Therefore, this study aims to identify these challenges and provide strategies to improve the national infrastructure for weather data and information. In this regard, based on a review of literature and documents, as interviews and discussions with experts conducted from late November to mid-February 2022, the strengths, weaknesses, opportunities, and threats (SWOT) of the country's weather data infrastructure were identified. Strategies for improving this infrastructure were then derived using the SWOT model. These strategies were weighted and prioritized in three areas: policy, socio-economic, and technical. The most important strategies, in order of priority, include: 1- Establishing a national system aimed at providing quick and easy access to specialized weather information, while creating a basic statistical concept registry at the prototype level, 2- The application of innovative equipment and advanced technologies for the extraction and comprehensive access to data enhances accuracy and increases the reliability of analytical reports, 3- Defining, determining, and approving an official authority and appropriate cross-sectoral structure for standardizing, supervising, and revising the process of weather data and information dissemination, and 4- Amending laws and removing governance barriers related to weather data dissemination. Focusing on these strategies and developing operational plans to achieve them is one of the key actions and policies for improving water governance in Iran.
Keywords
Water policy
weather data
SWOT model
Strategies
National Infrastructure of Weather ‎Data
Subjects
منابع فارسی
افسری، ع.، حاجی ناصری، س.، فاضلی، م. و فیرحی، د. (1396). مدلِ داده ‌بنیاد بررسی جامعه‌شناختی حکمرانی آب در بحرانِ دریاچه‌ی ارومیه. مطالعات راهبردی سیاستگذاری عمومی، 7(25), 53-72.
دبیرخانه شورای عالی آب، مصوبات کارگروه کارشناسی شورای عالی آب. (1398).
حب وطن، م.، حیدری، ن.، جعفری، ب.، ارشدی، م.، لطفی، س. و ضرغامی، م. (1399). تحلیل راهبردی برای بهبود کارکرد و اقتدارشورای عالی آب با استفاده از روش  SWOT. مجله تحقیقات منابع آب ایران، شماره 4. 
فتاحی، س.، (1397). گزارش ملی آب و سیاست‌گذاری مبتنی بر پیچیدگی. مطالعات راهبردی سیاستگذاری عمومی، 8(27)، 53-72
 عبدالحسین زاده، م.، ثنایی، م. و ذوالفقارزاده، م م.، (1396). مفهوم شناسی سیاستگذاری داده باز حاکمیتی و تبیین مزایا و فواید آن در عرصه‌های مختلف سیاستگذاری. مطالعات راهبردی سیاستگذاری عمومی، 7(22), 74-55.
میرعمادی، س. ا.، ضرغامی، م.، و طباطبایی، س. م.، (1403). تحلیل موانع نهادی حکمرانی داده‌محور در سطوح سازمانی و بین‌سازمانی؛ مطالعه‌ی موردی دسترسی اشتراکی به داده‌های آب در ایران. مطالعات راهبردی سیاستگذاری عمومی، 14(51 ویژه‌نامه), 82-103.
 doi: 10.22034/sspp.2024.2033080.3667
References
Amegnaglo, C. J., Anaman, K. A., Mensah-Bonsu, A., Onumah, E. E., & Gero, F. A. (2017). Contingent valuation study of the benefits of seasonal climate forecasts for maize farmers in the Republic of Benin, West Africa. Climate Services, 6, 1-11. https://www.sciencedirect.com/science/article/pii/S2405880716300620. 
Anaman, K.A. & Lellyett. S.C. (1996). Assessment of the Benefits of an Enhanced Weather Information Service for the Cotton Industry in Australia. Meteorological Applications. 3(2): p. 127-135. [https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/met.5060030203].
Australia Bureau of Meteorology, Data Services. (2021). http://reg.bom.gov.au.
Benzaghta, M. A., Elwalda, A., Mousa, M. M., Erkan, I., & Rahman, M. (2021). SWOT Analysis Applications: An Integrative literature Review. Journal of Global Business Insights, 6(1), 54-72. https://pdfs.semanticscholar.org.
Blair, P. and Buytaert, W. (2016). Socio-Hydrological Modelling: a Review Asking "why, what and how?". Hydrology and Earth System Sciences, 20(1), 443-478. https://doi.org/10.5194/hess-20-443-2016.
Chen, Y., & Vardon, M. (2024). Accounting for water-related ecosystem services to provide information for water policy and management: An Australian case study. Ecosystem Services, 69, 101658.
Colohan, P., & Onda, K. (2022). Water Data for Water Science and Management: Advancing an Internet of Water (IoW). PLOS Water, 1(3), e0000017. https://journals.plos.org/water/article?id=10.1371/journal.pwat.0000017.
Costello, C. J., Adams, R. M., & Polasky, S. (1998). The Value of El Niño Forecasts in the Management of Salmon: a Stochastic Dynamic Assessment. American Journal of Agricultural Economics, 80(4), 765-777. https://onlinelibrary.wiley.com/doi/abs/10.2307/1244062.
Das, N., Andreadis, K., & Ines, A. (2019). Monitoring and Forecasting of Seasonal Rice Crop Productivity for Paddy Dominated Regions of India. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XLII-3/W6, 201-209. https://doi.org/10.5194/isprs-archives-xlii-3-w6-201-2019.
El Esawey, M., Walker, S., Sowers, C., & Sengupta, J. (2019). Safety Assessment of the Integration of Road Weather Information Systems and Variable Message Signs in British Columbia. Transportation research record, 2673(4), 305-313. https://journals.sagepub.com/doi/abs/10.1177/0361198119840335.
Essenfelder, A., Pérez-Blanco, C., & Mayer, A. (2018). Rationalizing Systems Aanalysis for the Evaluation of Adaptation Strategies in Complex Human‐Water Systems. Earth S Future, 6(9), 1181-1206. https://doi.org/10.1029/2018ef000826.
Fakhruddin, B.S.H.M. and L. Schick. (2019). Benefits of Economic Assessment of Cyclone Early Warning Systems-A case Study on Cyclone Evan in Samoa. Progress in Disaster Science. 2: p. 100034-100034. https://www.sciencedirect.com/science/article/pii/S2590061719300341.
Felbermayr, G., Gröschl, J., Sanders, M., Schippers, V., & Steinwachs, T. (2022). The Economic Impact of Weather Anomalies. World Development, 151, 105745. https://www.sciencedirect.com/science/article/pii/S0305750X21003600.
Fiorillo, D., Kapelan, Z., Xenochristou, M., De Paola, F., & Giugni, M. (2021). Assessing the Impact of Climate Change on Future Water Demand Using Weather Data. Water Resources Management, 35(5), 1449-1462. https://link.springer.com/article/10.1007/s11269-021-02789-4.
Firoz, A., Nauditt, A., Fink, M., & Ribbe, L. (2018). Quantifying Human Impacts on Hydrological Drought Using a Combined Modelling Approach in a Tropical River Basin in Central Vietnam. Hydrology and Earth System Sciences, 22(1), 547-565. https://doi.org/10.5194/hess-22-547-2018.
Frei, T., S. von Grünigen, and S. Willemse. (2014). Economic Benefit of Meteorology in the Swiss Road Transportation Sector. Meteorological Applications. 21(2): p. 294-300. https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/met.1329.
Frei, T. (2010). Economic and Social Benefits of Meteorology and Climatology in Switzerland. Meteorological Applications: A journal of forecasting, practical applications, training techniques and modelling. 17(1): p. 39-44. https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/met.156.
Gal, Adiv.(2023). Strengths, weaknesses, opportunities and threats: A SWOT analysis of a long-term Outdoor Environmental Education Program in Israel. Journal of Outdoor and Environmental Education: 1-19. https://link.springer.com/article/10.1007/s42322-023-00125-5.
Gardner, J., Doyle, M., & Patterson, L. (2017). Estimating the Value of Public Water Data. https://nicholasinstitute.duke.edu/content/estimating-value-public-water-data.
Gober, P. and Wheater, H. (2015). Debates—perspectives on socio‐hydrology: Modeling Flood Risk as a Public Policy Problem. Water Resources Research, 51(6), 4782-4788. https://doi.org/10.1002/2015wr016945.
Hallegatte, S. (2012). A Cost Effective Solution to Reduce Disaster Losses in Developing Countries: hydro-meteorological services, early warning, and evacuation. World Bank policy research working paper. (6058). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2051341.
Hassan, I., Kalin, R., White, C., & Aladejana, J. (2020). Evaluation of Daily Gridded Meteorological Datasets over the Niger Delta Region of Nigeria and Implication to Water Resources Management. Atmospheric and Climate Sciences, 10(01), 21-39. https://doi.org/10.4236/acs.2020.101002.
He, C., Yao, Y., Lu, X., Chen, M., Ma, W., & Zhou, L. (2019). Exploring the Influence Mechanism of Meteorological Conditions on the Concentration of Suspended Solids and Chlorophyll-a in large Estuaries based on Modis Imagery. Water, 11(2), 375. https://doi.org/10.3390/w11020375.
Horne, J. (2015). Water Information as a Tool to Enhance Sustainable Water management-the Australian experience. Water, 7(5), 2161-2183. https://www.mdpi.com/2073-4441/7/5/2161.
IWRM Data Portal– IOWater. (2017). https://iwrmdataportal.unepdhi.org.
Koffi, B., Kouadio, Z., Yao, A., Sanchez, M., & Kouassı, K. (2020). Impact of Meteorological Drought on Streamflows in the Lobo River Catchment at Nibéhibé, Côte d’Ivoire. Journal of Water Resource and Protection, 12(06), 495-511. https://doi.org/10.4236/jwarp.2020.126030.
Lazo, J.K. (2015). Survey of Mozambique Public on Weather, Water, and Climate Information. National Center for Atmospheric Research Technical Notes. NCAR/TN-521+ STR. Colorado.
Leviäkangas, P., & Hautala, R. (2009). Benefits and Value of Meteorological Information Services—the Case of the Finnish Meteorological Institute». Meteorological Applications: A journal of forecasting, practical applications, training techniques and modelling, 16(3), 369-379. https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/met.135.
Liao, S.-Y., C.-C. Chen, and S.-H. Hsu. (2010). Estimating the value of El Nino Southern Oscillation Information in a Regional Water Market with Implications for Water Management. Journal of Hydrology. 394(3-4): p. 347-356. https://www.sciencedirect.com/science/article/abs/pii/S0022169410005883.
Nasiri, N., Asghari, K., & Besalatpour, A. (2022). Quantitative Analysis of the Human Intervention Impacts on Hydrological Drought in the Zayande-rud River Basin, Iran. Journal of Water and Climate Change, 13(9), 3473-3495. https://doi.org/10.2166/wcc.2022.188.
National Environmental Satellite Data and Information Service. (2016). NOAA’s National Centers for Environmental Information. https://www.nesdis.noaa.gov.
Perez, R. (2023). Hydrological Modeling Based on Weather Forecasting for Effective Water Resource Management in the Piracicaba River Basin, Brazil. https://doi.org/10.21203/rs.3.rs-3778249/v1.
Patterson. L, Doyle. M, King. M, Monsma. D. (2017). Internet of water: sharing and integrating water data for sustainability. The Aspen Institute Dialogue Series on Water Data. Washington (District of Columbia): The Aspen Institute. 33 p.
Prieto, M., Fragkou, M. C., & Calderón, M. (2020). Water Policy and Management in Chile. Encyclopedia of Water: Science, Technology, and Society. Wiley-Blackwell, Hoboken, 2-589. https://www.researchgate.net/profile/Manuel-Prieto-4.
Rajah, J. (2024). Understanding Hydrologic, Human, and Climate System Feedback Loops: Results of a Participatory Modeling Workshop. Water, 16(3), 396. https://doi.org/10.3390/w16030396.
Roebber, P. J., & Smith, S. (2023). Prospects for Machine Learning Activity within the United States National Weather Service. Bulletin of the American Meteorological Society, 104(7), E1333-E1344.
Rossi, L., Regni, L., Rinaldi, S., Sdringola, P., Calisti, R., Brunori, A., … & Proietti, P. (2019). Long-Term Water Footprint Assessment in a Rainfed Olive Tree grove in the Umbria Region, Italy. Agriculture, 10(1), 8. https://doi.org/10.3390/agriculture10010008.
Tercini, J. (2023). Impact of Hydroclimatic Changes on Water Security in the Cantareira Water Production System, Brazil. Atmosphere, 14(12). https://doi.org/10.3390/atmos14121836.
The Western States Water Council (WSWC). (2024) .Water Data Exchange (WaDE) Program, https://westernstateswater.org/wade.
Usman, M., Ndehedehe, C., Farah, H., Ahmad, B., Wong, Y., & Adeyeri, O. (2022). Application of a Conceptual Hydrological Model for Streamflow Prediction Using Multi-Source Precipitation Products in a Semi-arid River Basin. Water, 14(8), 1260. https://doi.org/10.3390/w14081260.
Vertessy, R. A. (2013). Water Information Services for Australians. Australasian Journal of Water Resources, 16(2). 91-105. https://www.tandfonline.com/doi/abs/10.7158/13241583.2013.11465407.
Wang, H., Song, S., Zhang, G., Ayantobo, O., & Guo, T. (2023). Stochastic Volatility Modeling of Daily Streamflow Time Series. Water Resources Research, 59(1). https://doi.org/10.1029/2021wr031662.
Water Information System for Europe. (2024). https://water.europa.eu.
WMO.(2022). WMO Unified Data Policy. Retrieved from https://library.wmo.int/records/item/58009-wmo-unified-data-policy.
Yao, Y., Chen, K., Andrews, C., Scanlon, B., Kuang, X., Zeng, Z., … & Li, G. (2021). Role of Groundwater in Sustaining Northern Himalayan Rivers. Geophysical Research Letters, 48(10). https://doi.org/10.1029/2020gl092354.
Strategic Studies of public policy
Volume 15, Issue 54
Special on Water Policy Making
Spring 2025
Pages 94-117