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Evaluation of the performance of satellite products and microphysical schemes with the aim of forecasting early flood warnings in arid and semi-arid regions (a case study of northeastern Iran)

Author

Listed:
  • Rasoul Sarvestan

    (Hakim Sabzevari University)

  • Reza Barati

    (Applied Research Department, Khorasan Razavi Regional Water Company)

  • Aliakbar Shamsipour

    (University of Tehran)

  • Sahar Khazaei

    (Regional water company of khorasan razavi)

  • Manfred Kleidorfer

    (University of Innsbruck)

Abstract

Flood early warning requires rainfall data with a high temporal and spatial resolution for flood risk analysis to simulate flood dynamics in all small and large basins. However, such high-quality data are still very scarce in many developing countries. In this research, in order to identify the best and most up-to-date rainfall estimation tools for early flood forecasting in arid and semi-arid regions, the northeastern region of Iran with 17 meteorological stations and four rainfall events was investigated. The rainfall products of satellites (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record, Global Satellite Mapping of Precipitation, Climate Hazards Group InfraRed Precipitation with Station, European Reanalysis (ERA5), Global Precipitation Measurement) along with the most widely used microphysical schemes of Weather Research and Forecasting (WRF) model (Purdue-Lin (Lin), WRF Single-Moment class 3, 6, and WRF Double-Moment class 6. were used for rainfall modeling. The efficiency of each of these models to forecasting the amount of rainfall was verified by four methods: Threat Scores (TS), False Alarm Ratio, Hit Rate (H), and False Alarm (F). Analysis of research findings showed that the WRF meteorological model has better accuracy in rainfall modeling for the next 24 h. In this model, Lin's microphysical scheme has the highest accuracy, and its threat score (TS) quantity is up to 98% efficient in some stations. The best accuracy of satellite products for estimating the amount of rainfall is up to 50%. This accuracy value is related to the satellite product (ERA5). In this method, an 18 km distance from the ground station is the best distance for setting up the space station, which is used for input to hydrological/hydraulic models. Based on the results of this research, by using the connection of the WRF model with hydrology/hydraulic models, it is possible to predict and simulate rainfall-runoff up to 72 h before its occurrence. Also, by using these space stations, the amount of rainfall is estimated for the entire area of the basin and an early flood warning is issued.

Suggested Citation

  • Rasoul Sarvestan & Reza Barati & Aliakbar Shamsipour & Sahar Khazaei & Manfred Kleidorfer, 2024. "Evaluation of the performance of satellite products and microphysical schemes with the aim of forecasting early flood warnings in arid and semi-arid regions (a case study of northeastern Iran)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 12401-12426, October.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:13:d:10.1007_s11069-024-06689-9
    DOI: 10.1007/s11069-024-06689-9
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    References listed on IDEAS

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    1. Jennifer Kreklow & Björn Tetzlaff & Gerald Kuhnt & Benjamin Burkhard, 2019. "A Rainfall Data Intercomparison Dataset of RADKLIM, RADOLAN, and Rain Gauge Data for Germany," Data, MDPI, vol. 4(3), pages 1-16, August.
    2. Bhuvanamitra Sulugodu & Paresh Chandra Deka, 2019. "Evaluating the Performance of CHIRPS Satellite Rainfall Data for Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3913-3927, September.
    3. Pratyush Tripathy & Teja Malladi, 2022. "Global Flood Mapper: a novel Google Earth Engine application for rapid flood mapping using Sentinel-1 SAR," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 1341-1363, November.
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