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)
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...
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| description | 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. |
| doi_str_mv | 10.1007/s11069-024-06689-9 |
| format | Article |
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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. 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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.</description><subject>Accuracy</subject><subject>Arid zones</subject><subject>Artificial neural networks</subject><subject>Civil Engineering</subject><subject>Climate</subject><subject>Climatic data</subject><subject>Data analysis</subject><subject>Developing countries</subject><subject>Distance</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environmental hazards</subject><subject>Environmental Management</subject><subject>Environmental risk</subject><subject>Estimation</subject><subject>False alarms</subject><subject>Flood forecasting</subject><subject>Flood predictions</subject><subject>Flood risk</subject><subject>Flood warnings</subject><subject>Floods</subject><subject>Geophysics/Geodesy</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Global precipitation</subject><subject>Ground stations</subject><subject>Hazard identification</subject><subject>Hydraulic models</subject><subject>Hydrogeology</subject><subject>Hydrologic data</subject><subject>Hydrologic models</subject><subject>Hydrology</subject><subject>LDCs</subject><subject>Modelling</subject><subject>Natural Hazards</subject><subject>Neural networks</subject><subject>Original Paper</subject><subject>Precipitation</subject><subject>Precipitation estimation</subject><subject>Precipitation measurements</subject><subject>Rainfall</subject><subject>Rainfall data</subject><subject>Rainfall estimation</subject><subject>Rainfall runoff</subject><subject>Rainfall simulators</subject><subject>Rainfall-runoff relationships</subject><subject>Remote sensing</subject><subject>Risk analysis</subject><subject>Satellites</subject><subject>Semi arid areas</subject><subject>Semiarid lands</subject><subject>Semiarid zones</subject><subject>Space stations</subject><subject>Spatial data</subject><subject>Spatial discrimination</subject><subject>Spatial resolution</subject><subject>Weather forecasting</subject><subject>Weather stations</subject><issn>0921-030X</issn><issn>1573-0840</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kUFv1DAQhS1EJZaWP8DJEpdyMIydTZwcUVWgUiUuVOJmOc5k11ViLx6n1f67_jScXSRunCyP3_vGeo-x9xI-SQD9maSEphOgtgKapu1E94ptZK0rAe0WXrMNdEoKqODXG_aW6BFAykZ1G_Zy-2SnxWYfA48jz3vkB0xjTLMNDtcR2YzT5HN5SHFYXCZuw8Bn71I87I_knZ04uT3OSPzZ5_0JYv28mgsInaXsw46jTdORj1OMA3-2KZQZcR-4TX44IQlnL063hLvyIeLXlhc3csrLcFx5IaZCL0BMgd8lGz5esYvRToTv_p6X7OHr7c-b7-L-x7e7my_3wikNWWisVKvqrnejq2EAi3rosbVjyafuncLa6V6r3pVc7Og0NHW7bUtMzbBtXIvVJftw5pYUfi9I2TzGJYWy0lRSaqVAQldU6qwq4RAlHM0h-dmmo5Fg1qbMuSlTmjKnpsxqqs4mKuKww_QP_R_XHzh0myM</recordid><startdate>20241001</startdate><enddate>20241001</enddate><creator>Sarvestan, Rasoul</creator><creator>Barati, Reza</creator><creator>Shamsipour, Aliakbar</creator><creator>Khazaei, Sahar</creator><creator>Kleidorfer, Manfred</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>SOI</scope><orcidid>https://orcid.org/0009-0003-6544-1405</orcidid></search><sort><creationdate>20241001</creationdate><title>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)</title><author>Sarvestan, Rasoul ; 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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.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11069-024-06689-9</doi><tpages>26</tpages><orcidid>https://orcid.org/0009-0003-6544-1405</orcidid></addata></record> |
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| subjects | Accuracy Arid zones Artificial neural networks Civil Engineering Climate Climatic data Data analysis Developing countries Distance Earth and Environmental Science Earth Sciences Environmental hazards Environmental Management Environmental risk Estimation False alarms Flood forecasting Flood predictions Flood risk Flood warnings Floods Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Global precipitation Ground stations Hazard identification Hydraulic models Hydrogeology Hydrologic data Hydrologic models Hydrology LDCs Modelling Natural Hazards Neural networks Original Paper Precipitation Precipitation estimation Precipitation measurements Rainfall Rainfall data Rainfall estimation Rainfall runoff Rainfall simulators Rainfall-runoff relationships Remote sensing Risk analysis Satellites Semi arid areas Semiarid lands Semiarid zones Space stations Spatial data Spatial discrimination Spatial resolution Weather forecasting Weather stations |
| title | 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) |
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